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GCP - Professional Data Engineer1_2
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Questions in this quiz (200)
1You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics. Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?
- AAdd capacity (memory and disk space) to the database server by the order of 200
- BPartition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.
- CShard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
- DNormalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.
2You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to
BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?- ASet up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.
- BUse Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.
- CDevelop an App Engine application to schedule and request the status of the jobs using GCP API calls.
- DCreate a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.
3Your company is in the process of migrating its on-premises data warehousing solutions to BigQuery. The existing data warehouse uses trigger-based change data capture (CDC) to apply updates from multiple transactional database sources on a daily basis. With BigQuery, your company hopes to improve its handling of
CDC so that changes to the source systems are available to query in BigQuery in near-real time using log-based CDC streams, while also optimizing for the performance of applying changes to the data warehouse. Which two steps should they take to ensure that changes are available in the BigQuery reporting table with minimal latency while reducing compute overhead? (Choose two.)- APeriodically DELETE outdated records from the reporting table.
- BInsert each new CDC record and corresponding operation type in real time to the reporting table, and use a materialized view to expose only the newest version of each unique record.
- CPerform a DML INSERT, UPDATE, or DELETE to replicate each individual CDC record in real time directly on the reporting table.
- DPeriodically use a DML MERGE to perform several DML INSERT, UPDATE, and DELETE operations at the same time on the reporting table.
- EInsert each new CDC record and corresponding operation type to a staging table in real time.
4You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud
Stackdriver to ensure that it is processing data. Which Stackdriver alerts should you create?- AAn alert based on an increase of instance/storage/used_bytes for the source and a rate of change decrease of subscription/ num_undelivered_messages for the destination
- BAn alert based on a decrease of instance/storage/used_bytes for the source and a rate of change increase of subscription/ num_undelivered_messages for the destination
- CAn alert based on an increase of subscription/num_undelivered_messages for the source and a rate of change decrease of instance/storage/ used_bytes for the destination
- DAn alert based on a decrease of subscription/num_undelivered_messages for the source and a rate of change increase of instance/storage/ used_bytes for the destination
5Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects.
What should you do?- ACreate an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Cloud Monitoring dashboard based on the custom metric
- BCreate a Cloud Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
- CCreate a Cloud Monitoring dashboard based on the BigQuery metric query/scanned_bytes
- DCreate a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Cloud Monitoring dashboard based on the custom metric
6You want to schedule a number of sequential load and transformation jobs. Data files will be added to a Cloud Storage bucket by an upstream process. There is no fixed schedule for when the new data arrives. Next, a Dataproc job is triggered to perform some transformations and write the data to BigQuery. You then need to run additional transformation jobs in BigQuery. The transformation jobs are different for every table. These jobs might take hours to complete. You need to determine the most efficient and maintainable workflow to process hundreds of tables and provide the freshest data to your end users. What should you do?
- A1. Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators.
2. Use a single shared DAG for all tables that need to go through the pipeline.
3. Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG. - B1. Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage, Dataproc, and BigQuery operators.
2. Use a single shared DAG for all tables that need to go through the pipeline.
3. Schedule the DAG to run hourly. - C1. Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators.
2. Create a separate DAG for each table that needs to go through the pipeline.
3. Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG. - D1. Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage, Dataproc, and BigQuery operators.
2. Create a separate DAG for each table that needs to go through the pipeline.
3. Schedule the DAGs to run hourly.
- A1. Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators.
7You work for an advertising company, and you've developed a Spark ML model to predict click-through rates at advertisement blocks. You've been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be closing soon, so a rapid lift-and-shift migration is necessary. However, the data you've been using will be migrated to migrated to BigQuery. You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?
- ARewrite your models on TensorFlow, and start using Vertex AI
- BUse Dataproc for training existing Spark ML models, but start reading data directly from BigQuery
- CUse Vertex AI for training existing Spark ML models
- DSpin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery
8You need to modernize your existing on-premises data strategy. Your organization currently uses:
• Apache Hadoop clusters for processing multiple large data sets, including on-premises Hadoop Distributed File System (HDFS) for data replication.
• Apache Airflow to orchestrate hundreds of ETL pipelines with thousands of job steps.
You need to set up a new architecture in Google Cloud that can handle your Hadoop workloads and requires minimal changes to your existing orchestration processes. What should you do?- AUse Bigtable for your large workloads, with connections to Cloud Storage to handle any HDFS use cases. Orchestrate your pipelines with Cloud Composer.
- BUse Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Convert your ETL pipelines to Dataflow.
- CUse Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Orchestrate your pipelines with Cloud Composer.
- DUse Dataproc to migrate your Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Use Cloud Data Fusion to visually design and deploy your ETL pipelines.
9Your company handles data processing for a number of different clients. Each client prefers to use their own suite of analytics tools, with some allowing direct query access via Google BigQuery. You need to secure the data so that clients cannot see each other's data. You want to ensure appropriate access to the data.
Which three steps should you take? (Choose three.)- ALoad data into different partitions.
- BOnly allow a service account to access the datasets.
- CRestrict a client's dataset to approved users.
- DPut each client's BigQuery dataset into a different table.
- ELoad data into a different dataset for each client.
- FUse the appropriate identity and access management (IAM) roles for each client's users.
10You are deploying a new storage system for your mobile application, which is a media streaming service. You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity 'Movie' the property 'actors' and the property
'tags' have multiple values but the property 'date released' does not. A typical query would ask for all movies with actor=<actorname> ordered by date_released or all movies with tag=Comedy ordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?- ASet the following in your entity options: exclude_from_indexes = 'actors, tags'
- BSet the following in your entity options: exclude_from_indexes = 'date_published'
- CManually configure the index in your index config as follows:
indexes:
- kind: Task
properties:
- name: tags
- name: created
- kind: Task
properties:
- name: collaborators
- name: created - DManually configure the index in your index config as follows:
11Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub streaming data, one of the important business requirements is to be able to periodically identify the inputs and their timings during their campaign. Engineers have decided to use windowing and transformation in Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?
- AThey have not assigned the timestamp, which causes the job to fail
- BThey have not applied a non-global windowing function, which causes the job to fail when the pipeline is created
- CThey have not applied a global windowing function, which causes the job to fail when the pipeline is created
- DThey have not set the triggers to accommodate the data coming in late, which causes the job to fail
12Your company wants to be able to retrieve large result sets of medical information from your current system, which has over 10 TBs in the database, and store the data in new tables for further query. The database must have a low-maintenance architecture and be accessible via SQL. You need to implement a cost-effective solution that can support data analytics for large result sets. What should you do?
- AUse Cloud Spanner to replicate the data across regions. Normalize the data in a series of tables.
- BUse Cloud SQL, but first organize the data into tables. Use JOIN in queries to retrieve data.
- CUse BigQuery as a data warehouse. Set output destinations for caching large queries.
- DUse a MySQL cluster installed on a Compute Engine managed instance group for scalability.
13You have a query that filters a BigQuery table using a WHERE clause on timestamp and ID columns. By using bq query `"-dry_run you learn that the query triggers a full scan of the table, even though the filter on timestamp and ID select a tiny fraction of the overall data. You want to reduce the amount of data scanned by BigQuery with minimal changes to existing SQL queries. What should you do?
- AUse the LIMIT keyword to reduce the number of rows returned.
- BUse the bq query --maximum_bytes_billed flag to restrict the number of bytes billed.
- CCreate a separate table for each ID.
- DRecreate the table with a partitioning column and clustering column.
14You migrated a data backend for an application that serves 10 PB of historical product data for analytics. Only the last known state for a product, which is about 10 GB of data, needs to be served through an API to the other applications. You need to choose a cost-effective persistent storage solution that can accommodate the analytics requirements and the API performance of up to 1000 queries per second (QPS) with less than 1 second latency. What should you do?
- A1. Store the historical data in Cloud SQL for analytics.
2. In a separate table, store the last state of the product after every product change.
3. Serve the last state data directly from Cloud SQL to the API. - B1. Store the historical data in BigQuery for analytics.
2. Use a materialized view to precompute the last state of a product.
3. Serve the last state data directly from BigQuery to the API. - C1. Store the historical data in BigQuery for analytics.
2. In a Cloud SQL table, store the last state of the product after every product change.
3. Serve the last state data directly from Cloud SQL to the API. - D1. Store the products as a collection in Firestore with each product having a set of historical changes.
2. Use simple and compound queries for analytics.
3. Serve the last state data directly from Firestore to the API.
- A1. Store the historical data in Cloud SQL for analytics.
15You work for a car manufacturer and have set up a data pipeline using Google Cloud Pub/Sub to capture anomalous sensor events. You are using a push subscription in Cloud Pub/Sub that calls a custom HTTPS endpoint that you have created to take action of these anomalous events as they occur. Your custom
HTTPS endpoint keeps getting an inordinate amount of duplicate messages. What is the most likely cause of these duplicate messages?- AYour custom endpoint has an out-of-date SSL certificate.
- BThe message body for the sensor event is too large.
- CThe Cloud Pub/Sub topic has too many messages published to it.
- DYour custom endpoint is not acknowledging messages within the acknowledgement deadline.
16Business owners at your company have given you a database of bank transactions. Each row contains the user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what type of machine learning can be applied to the data. Which three machine learning applications can you use? (Choose three.)
- AReinforcement learning to predict the location of a transaction.
- BUnsupervised learning to predict the location of a transaction.
- CUnsupervised learning to determine which transactions are most likely to be fraudulent.
- DSupervised learning to predict the location of a transaction.
- EClustering to divide the transactions into N categories based on feature similarity.
- FSupervised learning to determine which transactions are most likely to be fraudulent.
17You work for a global shipping company. You want to train a model on 40 TB of data to predict which ships in each geographic region are likely to cause delivery delays on any given day. The model will be based on multiple attributes collected from multiple sources. Telemetry data, including location in GeoJSON format, will be pulled from each ship and loaded every hour. You want to have a dashboard that shows how many and which ships are likely to cause delays within a region. You want to use a storage solution that has native functionality for prediction and geospatial processing. Which storage solution should you use?
- ABigQuery
- BCloud SQL for PostgreSQL
- CCloud Datastore
- DCloud Bigtable
18You are deploying a batch pipeline in Dataflow. This pipeline reads data from Cloud Storage, transforms the data, and then writes the data into BigQuery. The security team has enabled an organizational constraint in Google Cloud, requiring all Compute Engine instances to use only internal IP addresses and no external IP addresses. What should you do?
- AEnsure that the firewall rules allow access to Cloud Storage and BigQuery. Use Dataflow with only internal IPs.
- BCreate a VPC Service Controls perimeter that contains the VPC network and add Dataflow, Cloud Storage, and BigQuery as allowed services in the perimeter. Use Dataflow with only internal IP addresses.
- CEnsure that your workers have network tags to access Cloud Storage and BigQuery. Use Dataflow with only internal IP addresses.
- DEnsure that Private Google Access is enabled in the subnetwork. Use Dataflow with only internal IP addresses.
19You are on the data governance team and are implementing security requirements to deploy resources. You need to ensure that resources are limited to only the europe-west3 region. You want to follow Google-recommended practices.
What should you do?- ADeploy resources with Terraform and implement a variable validation rule to ensure that the region is set to the europe-west3 region for all resources.
- BSet the constraints/gcp.resourceLocations organization policy constraint to in:eu-locations.
- CSet the constraints/gcp.resourceLocations organization policy constraint to in:europe-west3-locations.
- DCreate a Cloud Function to monitor all resources created and automatically destroy the ones created outside the europe-west3 region.
20You are running a pipeline in Dataflow that receives messages from a Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)
- AUse a larger instance type for your Dataflow workers
- BCreate a temporary table in Cloud Spanner that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery
- CCreate a temporary table in Bigtable that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Bigtable to BigQuery
- DChange the zone of your Dataflow pipeline to run in us-central1
- EIncrease the number of max workers
21Data Analysts in your company have the Cloud IAM Owner role assigned to them in their projects to allow them to work with multiple GCP products in their projects. Your organization requires that all BigQuery data access logs be retained for 6 months. You need to ensure that only audit personnel in your company can access the data access logs for all projects. What should you do?
- AEnable data access logs in each Data Analyst's project. Restrict access to Stackdriver Logging via Cloud IAM roles.
- BExport the data access logs via a project-level export sink to a Cloud Storage bucket in a newly created projects for audit logs. Restrict access to the project with the exported logs.
- CExport the data access logs via a project-level export sink to a Cloud Storage bucket in the Data Analysts' projects. Restrict access to the Cloud Storage bucket.
- DExport the data access logs via an aggregated export sink to a Cloud Storage bucket in a newly created project for audit logs. Restrict access to the project that contains the exported logs.
22You are collecting IoT sensor data from millions of devices across the world and storing the data in BigQuery. Your access pattern is based on recent data, filtered by location_id and device_version with the following query:
You want to optimize your queries for cost and performance. How should you structure your data?
- APartition table data by create_date, cluster table data by location_id, and device_version.
- BCluster table data by create_date, partition by location_id, and device_version.
- CCluster table data by create_date, location_id, and device_version.
- DPartition table data by create_date, location_id, and device_version.
23You are operating a streaming Cloud Dataflow pipeline. Your engineers have a new version of the pipeline with a different windowing algorithm and triggering strategy. You want to update the running pipeline with the new version. You want to ensure that no data is lost during the update. What should you do?
- AStop the Cloud Dataflow pipeline with the Cancel option. Create a new Cloud Dataflow job with the updated code
- BUpdate the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to a new unique job name
- CUpdate the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to the existing job name
- DStop the Cloud Dataflow pipeline with the Drain option. Create a new Cloud Dataflow job with the updated code
24You want to use Google Stackdriver Logging to monitor Google BigQuery usage. You need an instant notification to be sent to your monitoring tool when new data is appended to a certain table using an insert job, but you do not want to receive notifications for other tables. What should you do?
- AUsing the Stackdriver API, create a project sink with advanced log filter to export to Pub/Sub, and subscribe to the topic from your monitoring tool.
- BMake a call to the Stackdriver API to list all logs, and apply an advanced filter.
- CIn the Stackdriver logging admin interface, enable a log sink export to Google Cloud Pub/Sub, and subscribe to the topic from your monitoring tool.
- DIn the Stackdriver logging admin interface, and enable a log sink export to BigQuery.
25You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of-Things (IoT) devices. The volume of data is growing at 100
TB per year, and each data entry has about 100 attributes. The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID).
However, high availability and low latency are required.
You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose three.)- ACassandra
- BHDFS with Hive
- CHBase
- DRedis
- EMySQL
- FMongoDB
26You need to give new website users a globally unique identifier (GUID) using a service that takes in data points and returns a GUID. This data is sourced from both internal and external systems via HTTP calls that you will make via microservices within your pipeline. There will be tens of thousands of messages per second and that can be multi-threaded. and you worry about the backpressure on the system. How should you design your pipeline to minimize that backpressure?
- ABatch the job into ten-second increments.
- BCreate the pipeline statically in the class definition.
- CCreate a new object in the startBundle method of DoFn.
- DCall out to the service via HTTP.
27Your team is building a data lake platform on Google Cloud. As a part of the data foundation design, you are planning to store all the raw data in Cloud Storage. You are expecting to ingest approximately 25 GB of data a day and your billing department is worried about the increasing cost of storing old data. The current business requirements are:
• The old data can be deleted anytime.
• There is no predefined access pattern of the old data.
• The old data should be available instantly when accessed.
• There should not be any charges for data retrieval.
What should you do to optimize for cost?- ACreate an Object Lifecycle Management policy to modify the storage class for data older than 30 days to nearline, 45 days to coldline, and 60 days to archive storage class. Delete old data as needed.
- BCreate the bucket with the Autoclass storage class feature.
- CCreate an Object Lifecycle Management policy to modify the storage class for data older than 30 days to coldline, 90 days to nearline, and 365 days to archive storage class. Delete old data as needed.
- DCreate an Object Lifecycle Management policy to modify the storage class for data older than 30 days to nearline, 90 days to coldline, and 365 days to archive storage class. Delete old data as needed.
28An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application. They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?
- ACloud BigTable
- BBigQuery
- CCloud SQL
- DCloud Datastore
29You want to build a managed Hadoop system as your data lake. The data transformation process is composed of a series of Hadoop jobs executed in sequence.
To accomplish the design of separating storage from compute, you decided to use the Cloud Storage connector to store all input data, output data, and intermediary data. However, you noticed that one Hadoop job runs very slowly with Cloud Dataproc, when compared with the on-premises bare-metal Hadoop environment (8-core nodes with 100-GB RAM). Analysis shows that this particular Hadoop job is disk I/O intensive. You want to resolve the issue. What should you do?- AAllocate sufficient persistent disk space to the Hadoop cluster, and store the intermediate data of that particular Hadoop job on native HDFS
- BAllocate additional network interface card (NIC), and configure link aggregation in the operating system to use the combined throughput when working with Cloud Storage
- CAllocate sufficient memory to the Hadoop cluster, so that the intermediary data of that particular Hadoop job can be held in memory
- DAllocate more CPU cores of the virtual machine instances of the Hadoop cluster so that the networking bandwidth for each instance can scale up
30You used Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?
- ACreate a cron schedule in Dataprep.
- BExport the recipe as a Dataprep template, and create a job in Cloud Scheduler.
- CCreate an App Engine cron job to schedule the execution of the Dataprep job.
- DExport the Dataprep job as a Dataflow template, and incorporate it into a Composer job.
31Your car factory is pushing machine measurements as messages into a Pub/Sub topic in your Google Cloud project. A Dataflow streaming job, that you wrote with the Apache Beam SDK, reads these messages, sends acknowledgment to Pub/Sub, applies some custom business logic in a DoFn instance, and writes the result to BigQuery. You want to ensure that if your business logic fails on a message, the message will be sent to a Pub/Sub topic that you want to monitor for alerting purposes. What should you do?
- AEnable retaining of acknowledged messages in your Pub/Sub pull subscription. Use Cloud Monitoring to monitor the subscription/num_retained_acked_messages metric on this subscription.
- BEnable dead lettering in your Pub/Sub pull subscription, and specify a new Pub/Sub topic as the dead letter topic. Use Cloud Monitoring to monitor the subscription/dead_letter_message_count metric on your pull subscription.
- CCreate a snapshot of your Pub/Sub pull subscription. Use Cloud Monitoring to monitor the snapshot/num_messages metric on this snapshot.
- DUse an exception handling block in your Dataflow’s DoFn code to push the messages that failed to be transformed through a side output and to a new Pub/Sub topic. Use Cloud Monitoring to monitor the topic/num_unacked_messages_by_region metric on this new topic.
32MJTelco Case Study -
Company Overview -
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background -
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept -
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
✑ Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
✑ Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments `" development/test, staging, and production `" to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements -
✑ Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.
✑ Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
✑ Provide reliable and timely access to data for analysis from distributed research workers
Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Question
Technical Requirements -
✑ Ensure secure and efficient transport and storage of telemetry data
✑ Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
✑ Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately 100m records/day
✑ Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement -
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement -
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement -
The project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
You need to compose visualizations for operations teams with the following requirements:
✑ The report must include telemetry data from all 50,000 installations for the most resent 6 weeks (sampling once every minute).
✑ The report must not be more than 3 hours delayed from live data.
✑ The actionable report should only show suboptimal links.
✑ Most suboptimal links should be sorted to the top.
✑ Suboptimal links can be grouped and filtered by regional geography.
✑ User response time to load the report must be <5 seconds.
Which approach meets the requirements?- ALoad the data into Google Cloud Datastore tables, write a Google App Engine Application that queries all rows, applies a function to derive the metric, and then renders results in a table using the Google charts and visualization API.
- BLoad the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.
- CLoad the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a table.
- DLoad the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.
33You are migrating your data warehouse to BigQuery. You have migrated all of your data into tables in a dataset. Multiple users from your organization will be using the data. They should only see certain tables based on their team membership. How should you set user permissions?
- AAssign the users/groups data viewer access at the table level for each table
- BCreate authorized views for each team in datasets created for each team. Assign the authorized views data viewer access to the dataset in which the data resides. Assign the users/groups data viewer access to the datasets in which the authorized views reside
- CCreate SQL views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the SQL views
- DCreate authorized views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the authorized views
34You work for a large real estate firm and are preparing 6 TB of home sales data to be used for machine learning. You will use SQL to transform the data and use
BigQuery ML to create a machine learning model. You plan to use the model for predictions against a raw dataset that has not been transformed. How should you set up your workflow in order to prevent skew at prediction time?- AWhen creating your model, use BigQuery's TRANSFORM clause to define preprocessing steps. Before requesting predictions, use a saved query to transform your raw input data, and then use ML.EVALUATE.
- BPreprocess all data using Dataflow. At prediction time, use BigQuery's ML.EVALUATE clause without specifying any further transformations on the input data.
- CWhen creating your model, use BigQuery's TRANSFORM clause to define preprocessing steps. At prediction time, use BigQuery's ML.EVALUATE clause without specifying any transformations on the raw input data.
- DUse a BigQuery view to define your preprocessing logic. When creating your model, use the view as your model training data. At prediction time, use BigQuery's ML.EVALUATE clause without specifying any transformations on the raw input data.
35You want to create a machine learning model using BigQuery ML and create an endpoint for hosting the model using Vertex AI. This will enable the processing of continuous streaming data in near-real time from multiple vendors. The data may contain invalid values. What should you do?
- ACreate a Pub/Sub topic and send all vendor data to it. Use Dataflow to process and sanitize the Pub/Sub data and stream it to BigQuery.
- BCreate a new BigQuery dataset and use streaming inserts to land the data from multiple vendors. Configure your BigQuery ML model to use the "ingestion" dataset as the framing data.
- CCreate a Pub/Sub topic and send all vendor data to it. Connect a Cloud Function to the topic to process the data and store it in BigQuery.
- DUse BigQuery streaming inserts to land the data from multiple vendors where your BigQuery dataset ML model is deployed.
36You have some data, which is shown in the graphic below. The two dimensions are X and Y, and the shade of each dot represents what class it is. You want to classify this data accurately using a linear algorithm. To do this you need to add a synthetic feature. What should the value of that feature be?
- AX2+Y2
- Bcos(X)
- CX2
- DY2
37Your company receives both batch- and stream-based event data. You want to process the data using Google Cloud Dataflow over a predictable time period.
However, you realize that in some instances data can arrive late or out of order. How should you design your Cloud Dataflow pipeline to handle data that is late or out of order?- AUse watermarks and timestamps to capture the lagged data.
- BEnsure every datasource type (stream or batch) has a timestamp, and use the timestamps to define the logic for lagged data.
- CSet sliding windows to capture all the lagged data.
- DSet a single global window to capture all the data.
38You have a network of 1000 sensors. The sensors generate time series data: one metric per sensor per second, along with a timestamp. You already have 1 TB of data, and expect the data to grow by 1 GB every day. You need to access this data in two ways. The first access pattern requires retrieving the metric from one specific sensor stored at a specific timestamp, with a median single-digit millisecond latency. The second access pattern requires running complex analytic queries on the data, including joins, once a day. How should you store this data?
- AStore your data in BigQuery. Use the metric as a primary key.
- BStore your data in Bigtable. Concatenate the sensor ID and metric, and use it as the row key. Perform an export to BigQuery every day.
- CStore your data in BigQuery. Concatenate the sensor ID and timestamp, and use it as the primary key.
- DStore your data in Bigtable. Concatenate the sensor ID and timestamp and use it as the row key. Perform an export to BigQuery every day.
39You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?
- ACloud Natural Language API
- BSpeech-to-Text API
- CDialogflow Enterprise Edition
- DAutoML Natural Language
40You are running a Dataflow streaming pipeline, with Streaming Engine and Horizontal Autoscaling enabled. You have set the maximum number of workers to 1000. The input of your pipeline is Pub/Sub messages with notifications from Cloud Storage. One of the pipeline transforms reads CSV files and emits an element for every CSV line. The job performance is low, the pipeline is using only 10 workers, and you notice that the autoscaler is not spinning up additional workers. What should you do to improve performance?
- AUpdate the job to increase the maximum number of workers.
- BUse Dataflow Prime, and enable Right Fitting to increase the worker resources.
- CEnable Vertical Autoscaling to let the pipeline use larger workers.
- DChange the pipeline code, and introduce a Reshuffle step to prevent fusion.
41You are migrating your on-premises data warehouse to BigQuery. As part of the migration, you want to facilitate cross-team collaboration to get the most value out of the organization’s data. You need to design an architecture that would allow teams within the organization to securely publish, discover, and subscribe to read-only data in a self-service manner. You need to minimize costs while also maximizing data freshness. What should you do?
- AUse Analytics Hub to facilitate data sharing.
- BUse BigQuery Data Transfer Service to copy datasets to a centralized BigQuery project for sharing.
- CCreate a new dataset for sharing in each individual team’s project. Grant the subscribing team the bigquery.dataViewer role on the dataset.
- DCreate authorized datasets to publish shared data in the subscribing team's project.
42You need to choose a database for a new project that has the following requirements:
✑ Fully managed
✑ Able to automatically scale up
✑ Transactionally consistent
✑ Able to scale up to 6 TB
✑ Able to be queried using SQL
Which database do you choose?- ACloud Datastore
- BCloud SQL
- CCloud Bigtable
- DCloud Spanner
43You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?
- ACreate a table on Cloud Spanner, and insert and delete rows with the job information
- BCreate a table on Cloud SQL, and insert and delete rows with the job information
- CCreate an API using App Engine to receive and send messages to the applications
- DUse a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
44You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?- ACloud Bigtable
- BCloud Datastore
- CBigQuery
- DCloud SQL
45You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?
- AUse the TABLE_DATE_RANGE function
- BUse WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
- CUse SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
- DUse the WHERE_PARTITIONTIME pseudo column
46You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?
- ATrain your own image recognition model leveraging transfer learning techniques.
- BUse Cloud Vision AutoML, but reduce your dataset twice.
- CUse Cloud Vision API by providing custom labels as recognition hints.
- DUse Cloud Vision AutoML with the existing dataset.
47An aerospace company uses a proprietary data format to store its flight data. You need to connect this new data source to BigQuery and stream the data into
BigQuery. You want to efficiently import the data into BigQuery while consuming as few resources as possible. What should you do?- AUse a standard Dataflow pipeline to store the raw data in BigQuery, and then transform the format later when the data is used.
- BUse Apache Hive to write a Dataproc job that streams the data into BigQuery in CSV format.
- CUse an Apache Beam custom connector to write a Dataflow pipeline that streams the data into BigQuery in Avro format.
- DWrite a shell script that triggers a Cloud Function that performs periodic ETL batch jobs on the new data source.
48You need to migrate a 2TB relational database to Google Cloud Platform. You do not have the resources to significantly refactor the application that uses this database and cost to operate is of primary concern.
Which service do you select for storing and serving your data?- ACloud Bigtable
- BCloud Firestore
- CCloud SQL
- DCloud Spanner
49You have created an external table for Apache Hive partitioned data that resides in a Cloud Storage bucket, which contains a large number of files. You notice that queries against this table are slow. You want to improve the performance of these queries. What should you do?
- ACreate an individual external table for each Hive partition by using a common table name prefix. Use wildcard table queries to reference the partitioned data.
- BMigrate the Hive partitioned data objects to a multi-region Cloud Storage bucket.
- CUpgrade the external table to a BigLake table. Enable metadata caching for the table.
- DChange the storage class of the Hive partitioned data objects from Coldline to Standard.
50You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?
- ACreate a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
- BAdd a new column called FullName to the Users table. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
- CUse BigQuery to export the data for the table to a CSV file. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullName. Run a BigQuery load job to load the new CSV file into BigQuery.
- DCreate a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.
51You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?
- ACloud Functions
- BCloud Composer
- CCloud Dataflow
- DCloud Scheduler
52You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20
TB in size. Which database should you choose?- ACloud Spanner
- BCloud Datastore
- CCloud SQL
- DCloud Bigtable
53Your company is implementing a data warehouse using BigQuery, and you have been tasked with designing the data model. You move your on-premises sales data warehouse with a star data schema to BigQuery but notice performance issues when querying the data of the past 30 days. Based on Google's recommended practices, what should you do to speed up the query without increasing storage costs?
- AMaterialize the dimensional data in views.
- BDenormalize the data.
- CShard the data by customer ID.
- DPartition the data by transaction date.
54MJTelco Case Study -
Company Overview -
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background -
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept -
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
✑ Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
✑ Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments `" development/test, staging, and production `" to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements -
✑ Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.
✑ Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
✑ Provide reliable and timely access to data for analysis from distributed research workers
✑ Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements -
✑ Ensure secure and efficient transport and storage of telemetry data
✑ Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
✑ Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately 100m records/day
✑ Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement -
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement -
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement -
The project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
MJTelco's Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations. You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?- AThe maximun number of workers
- BThe zone
- CThe number of workers
- DThe disk size per worker
55Your team is responsible for developing and maintaining ETLs in your company. One of your Dataflow jobs is failing because of some errors in the input data, and you need to improve reliability of the pipeline (incl. being able to reprocess all failing data).
What should you do?- AAdd a try- catch block to your DoFn that transforms the data, extract erroneous rows from logs.
- BAdd a filtering step to skip these types of errors in the future, extract erroneous rows from logs.
- CAdd a try- catch block to your DoFn that transforms the data, use a sideOutput to create a PCollection that can be stored to Pub/Sub later.
- DAdd a try- catch block to your DoFn that transforms the data, write erroneous rows to Pub/Sub PubSub directly from the DoFn.
56Different teams in your organization store customer and performance data in BigQuery. Each team needs to keep full control of their collected data, be able to query data within their projects, and be able to exchange their data with other teams. You need to implement an organization-wide solution, while minimizing operational tasks and costs. What should you do?
- AEnable each team to create materialized views of the data they need to access in their projects.
- BCreate a BigQuery scheduled query to replicate all customer data into team projects.
- CAsk each team to publish their data in Analytics Hub. Direct the other teams to subscribe to them.
- DAsk each team to create authorized views of their data. Grant the biquery.jobUser role to each team.
57Your platform on your on-premises environment generates 100 GB of data daily, composed of millions of structured JSON text files. Your on-premises environment cannot be accessed from the public internet. You want to use Google Cloud products to query and explore the platform data. What should you do?
- AUse a Transfer Appliance to copy data from your on-premises environment to Cloud Storage. Use the BigQuery Data Transfer Service to import data into BigQuery.
- BUse Cloud Scheduler to copy data daily from your on-premises environment to Cloud Storage. Use the BigQuery Data Transfer Service to import data into BigQuery.
- CUse Transfer Service for on-premises data to copy data from your on-premises environment to Cloud Storage. Use the BigQuery Data Transfer Service to import data into BigQuery.
- DUse the BigQuery Data Transfer Service dataset copy to transfer all data into BigQuery.
58You want to archive data in Cloud Storage. Because some data is very sensitive, you want to use the `Trust No One` (TNO) approach to encrypt your data to prevent the cloud provider staff from decrypting your data. What should you do?
- AUse gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key. Use gsutil cp to upload each encrypted file to the Cloud Storage bucket. Manually destroy the key previously used for encryption, and rotate the key once.
- BSpecify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in Cloud Memorystore as permanent storage of the secret.
- CSpecify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in a different project that only the security team can access.
- DUse gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key and unique additional authenticated data (AAD). Use gsutil cp to upload each encrypted file to the Cloud Storage bucket, and keep the AAD outside of Google Cloud.
59You plan to deploy Cloud SQL using MySQL. You need to ensure high availability in the event of a zone failure. What should you do?
- ACreate a Cloud SQL instance in a region, and configure automatic backup to a Cloud Storage bucket in the same region.
- BCreate a Cloud SQL instance in one zone, and create a failover replica in another zone within the same region.
- CCreate a Cloud SQL instance in one zone, and create a read replica in another zone within the same region.
- DCreate a Cloud SQL instance in one zone, and configure an external read replica in a zone in a different region.
60You are building a model to make clothing recommendations. You know a user's fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available. How should you use this data to train the model?
- AContinuously retrain the model on a combination of existing data and the new data.
- BTrain on the existing data while using the new data as your test set.
- CContinuously retrain the model on just the new data.
- DTrain on the new data while using the existing data as your test set.
61Government regulations in your industry mandate that you have to maintain an auditable record of access to certain types of data. Assuming that all expiring logs will be archived correctly, where should you store data that is subject to that mandate?
- AIn a bucket on Cloud Storage that is accessible only by an AppEngine service that collects user information and logs the access before providing a link to the bucket.
- BIn Cloud SQL, with separate database user names to each user. The Cloud SQL Admin activity logs will be used to provide the auditability.
- CIn a BigQuery dataset that is viewable only by authorized personnel, with the Data Access log used to provide the auditability.
- DEncrypted on Cloud Storage with user-supplied encryption keys. A separate decryption key will be given to each authorized user.
62You are designing a fault-tolerant architecture to store data in a regional BigQuery dataset. You need to ensure that your application is able to recover from a corruption event in your tables that occurred within the past seven days. You want to adopt managed services with the lowest RPO and most cost-effective solution. What should you do?
- AAccess historical data by using time travel in BigQuery.
- BCreate a BigQuery table snapshot on a daily basis.
- CMigrate your data to multi-region BigQuery buckets.
- DExport the data from BigQuery into a new table that excludes the corrupted data
63You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
✑ Executing the transformations on a schedule
✑ Enabling non-developer analysts to modify transformations
✑ Providing a graphical tool for designing transformations
What should you do?- ALoad each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard schema. Merge the transformed tables together with a SQL query
- BUse Apache Spark on Dataproc to infer the schema of the CSV file before creating a Dataframe. Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery
- CHelp the analysts write a Dataflow pipeline in Python to perform the transformation. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
- DUse Dataprep by Trifacta to build and maintain the transformation recipes, and execute them on a scheduled basis
64You are a head of BI at a large enterprise company with multiple business units that each have different priorities and budgets. You use on-demand pricing for
BigQuery with a quota of 2K concurrent on-demand slots per project. Users at your organization sometimes don't get slots to execute their query and you need to correct this. You'd like to avoid introducing new projects to your account.
What should you do?- ACreate an additional project to overcome the 2K on-demand per-project quota.
- BConvert your batch BQ queries into interactive BQ queries.
- CSwitch to flat-rate pricing and establish a hierarchical priority model for your projects.
- DIncrease the amount of concurrent slots per project at the Quotas page at the Cloud Console.
65You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the
Data Science team runs a query filtered on a date column and limited to 30`"90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries.
What should you do?- ARecommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
- BRe-create the tables using DDL. Partition the tables by a column containing a TIMESTAMP or DATE Type.
- CWrite an Apache Beam pipeline that creates a BigQuery table per day. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
- DModify your pipeline to maintain the last 30ג€"90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.
66You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data. How can you adjust your application design?
- ARe-write the application to load accumulated data every 2 minutes.
- BEstimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.
- CConvert the streaming insert code to batch load for individual messages.
- DLoad the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.
67You need to choose a database to store time series CPU and memory usage for millions of computers. You need to store this data in one-second interval samples. Analysts will be performing real-time, ad hoc analytics against the database. You want to avoid being charged for every query executed and ensure that the schema design will allow for future growth of the dataset. Which database and data model should you choose?
- ACreate a narrow table in Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second
- BCreate a wide table in Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combine the values for each second as column data.
- CCreate a table in BigQuery, and append the new samples for CPU and memory to the table
- DCreate a wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for each second
68You architect a system to analyze seismic data. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?
- ADevelop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.
- BModify the transformMapReduce jobs to apply sensor calibration before they do anything else.
- CIntroduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
- DAdd sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
69You have data located in BigQuery that is used to generate reports for your company. You have noticed some weekly executive report fields do not correspond to format according to company standards. For example, report errors include different telephone formats and different country code identifiers. This is a frequent issue, so you need to create a recurring job to normalize the data. You want a quick solution that requires no coding. What should you do?
- ACreate a Spark job and submit it to Dataproc Serverless.
- BUse Cloud Data Fusion and Wrangler to normalize the data, and set up a recurring job.
- CUse Dataflow SQL to create a job that normalizes the data, and that after the first run of the job, schedule the pipeline to execute recurrently.
- DUse BigQuery and GoogleSQL to normalize the data, and schedule recurring queries in BigQuery.
70You operate a logistics company, and you want to improve event delivery reliability for vehicle-based sensors. You operate small data centers around the world to capture these events, but leased lines that provide connectivity from your event collection infrastructure to your event processing infrastructure are unreliable, with unpredictable latency. You want to address this issue in the most cost-effective way. What should you do?
- AEstablish a Cloud Interconnect between all remote data centers and Google.
- BWrite a Cloud Dataflow pipeline that aggregates all data in session windows.
- CHave the data acquisition devices publish data to Cloud Pub/Sub.
- DDeploy small Kafka clusters in your data centers to buffer events.
71Your startup has never implemented a formal security policy. Currently, everyone in the company has access to the datasets stored in Google BigQuery. Teams have freedom to use the service as they see fit, and they have not documented their use cases. You have been asked to secure the data warehouse. You need to discover what everyone is doing. What should you do first?
- AUse Google Stackdriver Audit Logs to review data access.
- BGet the identity and access management IIAM) policy of each table
- CUse the Google Cloud Billing API to see what account the warehouse is being billed to.
- DUse Stackdriver Monitoring to see the usage of BigQuery query slots.
72Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (Choose two.)- ARedesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
- BContact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
- CIntroduce data compression for each file to increase the rate file of file transfer.
- DAssemble 1,000 files into a tape archive (TAR) file. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
- ECreate an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premises data to the designated storage bucket.
73MJTelco Case Study -
Company Overview -
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background -
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept -
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
✑ Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
✑ Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments `" development/test, staging, and production `" to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements -
✑ Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.
✑ Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
Provide reliable and timely access to data for analysis from distributed research workers
Question
✑ Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements -
✑ Ensure secure and efficient transport and storage of telemetry data
✑ Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
✑ Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately 100m records/day
✑ Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement -
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement -
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement -
The project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
You create a new report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. It is company policy to ensure employees can view only the data associated with their region, so you create and populate a table for each region. You need to enforce the regional access policy to the data.
Which two actions should you take? (Choose two.)- AEnsure each table is included in a dataset for a region.
- BEnsure all the tables are included in global dataset.
- CAdjust the settings for each table to allow a related region-based security group view access.
- DAdjust the settings for each view to allow a related region-based security group view access.
- EAdjust the settings for each dataset to allow a related region-based security group view access.
74dataset.inventory_vm sample records:
You have an inventory of VM data stored in the BigQuery table. You want to prepare the data for regular reporting in the most cost-effective way. You need to exclude VM rows with fewer than 8 vCPU in your report. What should you do?
- ACreate a view with a filter to drop rows with fewer than 8 vCPU, and use the UNNEST operator.
- BCreate a materialized view with a filter to drop rows with fewer than 8 vCPU, and use the WITH common table expression.
- CUse Dataflow to batch process and write the result to another BigQuery table.
- DCreate a view with a filter to drop rows with fewer than 8 vCPU, and use the WITH common table expression.
75You need to copy millions of sensitive patient records from a relational database to BigQuery. The total size of the database is 10 TB. You need to design a solution that is secure and time-efficient. What should you do?
- AExport the records from the database as an Avro file. Copy the file onto a Transfer Appliance and send it to Google, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.
- BExport the records from the database as an Avro file. Create a public URL for the Avro file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.
- CExport the records from the database into a CSV file. Create a public URL for the CSV file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the CSV file into BigQuery using the BigQuery web UI in the GCP Console.
- DExport the records from the database as an Avro file. Upload the file to GCS using gsutil, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.
76Government regulations in the banking industry mandate the protection of clients' personally identifiable information (PII). Your company requires PII to be access controlled, encrypted, and compliant with major data protection standards. In addition to using Cloud Data Loss Prevention (Cloud DLP), you want to follow
Google-recommended practices and use service accounts to control access to PII. What should you do?- AUse Cloud Storage to comply with major data protection standards. Use multiple service accounts attached to IAM groups to grant the appropriate access to each group.
- BUse one service account to access a Cloud SQL database, and use separate service accounts for each human user.
- CUse Cloud Storage to comply with major data protection standards. Use one service account shared by all users.
- DAssign the required Identity and Access Management (IAM) roles to every employee, and create a single service account to access project resources.
77You are designing a basket abandonment system for an ecommerce company. The system will send a message to a user based on these rules:
✑ No interaction by the user on the site for 1 hour
Has added more than $30 worth of products to the basket
Question
✑ Has not completed a transaction
You use Google Cloud Dataflow to process the data and decide if a message should be sent. How should you design the pipeline?- AUse a fixed-time window with a duration of 60 minutes.
- BUse a global window with a time based trigger with a delay of 60 minutes.
- CUse a session window with a gap time duration of 60 minutes.
- DUse a sliding time window with a duration of 60 minutes.
78Your chemical company needs to manually check documentation for customer order. You use a pull subscription in Pub/Sub so that sales agents get details from the order. You must ensure that you do not process orders twice with different sales agents and that you do not add more complexity to this workflow. What should you do?
- ACreate a transactional database that monitors the pending messages.
- BUse Pub/Sub exactly-once delivery in your pull subscription.
- CUse a Deduplicate PTransform in Dataflow before sending the messages to the sales agents.
- DCreate a new Pub/Sub push subscription to monitor the orders processed in the agent's system.
79Your company is using WILDCARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the following error:
Which table name will make the SQL statement work correctly?
- Abigquery-public-data.noaa_gsod.gsod*
- B'bigquery-public-data.noaa_gsod.gsod*`
- C'bigquery-public-data.noaa_gsod.gsod'
- D'bigquery-public-data.noaa_gsod.gsod'*
80You are running a streaming pipeline with Dataflow and are using hopping windows to group the data as the data arrives. You noticed that some data is arriving late but is not being marked as late data, which is resulting in inaccurate aggregations downstream. You need to find a solution that allows you to capture the late data in the appropriate window. What should you do?
- AUse watermarks to define the expected data arrival window. Allow late data as it arrives.
- BChange your windowing function to session windows to define your windows based on certain activity.
- CChange your windowing function to tumbling windows to avoid overlapping window periods.
- DExpand your hopping window so that the late data has more time to arrive within the grouping.
81You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DT stores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRING type. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?
- AConstruct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DT into TIMESTAMP values. Run the query into a destination table NEW_CLICK_STREAM, in which the column TS is the TIMESTAMP type. Reference the table NEW_CLICK_STREAM instead of the table CLICK_STREAM from now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.
- BDelete the table CLICK_STREAM, and then re-create it such that the column DT is of the TIMESTAMP type. Reload the data.
- CAdd two columns to the table CLICK STREAM: TS of the TIMESTAMP type and IS_NEW of the BOOLEAN type. Reload all data in append mode. For each appended row, set the value of IS_NEW to true. For future queries, reference the column TS instead of the column DT, with the WHERE clause ensuring that the value of IS_NEW must be true.
- DAdd a column TS of the TIMESTAMP type to the table CLICK_STREAM, and populate the numeric values from the column TS for each row. Reference the column TS instead of the column DT from now on.
- ECreate a view CLICK_STREAM_V, where strings from the column DT are cast into TIMESTAMP values. Reference the view CLICK_STREAM_V instead of the table CLICK_STREAM from now on.
82Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?
- AThe CSV data loaded in BigQuery is not flagged as CSV.
- BThe CSV data has invalid rows that were skipped on import.
- CThe CSV data has not gone through an ETL phase before loading into BigQuery.
- DThe CSV data loaded in BigQuery is not using BigQuery's default encoding.
83You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristic support this method? (Choose two.)
- AYou expect future mutations to have different features from the mutated samples in the database.
- BYou already have labels for which samples are mutated and which are normal in the database.
- CThere are very few occurrences of mutations relative to normal samples.
- DThere are roughly equal occurrences of both normal and mutated samples in the database.
- EYou expect future mutations to have similar features to the mutated samples in the database.
84You need to migrate a Redis database from an on-premises data center to a Memorystore for Redis instance. You want to follow Google-recommended practices and perform the migration for minimal cost, time and effort. What should you do?
- AWrite a shell script to migrate the Redis data and create a new Memorystore for Redis instance.
- BMake a secondary instance of the Redis database on a Compute Engine instance and then perform a live cutover.
- CMake an RDB backup of the Redis database, use the gsutil utility to copy the RDB file into a Cloud Storage bucket, and then import the RDB file into the Memorystore for Redis instance.
- DCreate a Dataflow job to read the Redis database from the on-premises data center and write the data to a Memorystore for Redis instance.
85You are implementing security best practices on your data pipeline. Currently, you are manually executing jobs as the Project Owner. You want to automate these jobs by taking nightly batch files containing non-public information from Google Cloud Storage, processing them with a Spark Scala job on a Google Cloud
Dataproc cluster, and depositing the results into Google BigQuery.
How should you securely run this workload?- AUse a user account with the Project Viewer role on the Cloud Dataproc cluster to read the batch files and write to BigQuery
- BRestrict the Google Cloud Storage bucket so only you can see the files
- CUse a service account with the ability to read the batch files and to write to BigQuery
- DGrant the Project Owner role to a service account, and run the job with it
86Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer data. The data are imported to Cloud
Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?- AIncrease the CPU size on your server.
- BIncrease the size of the Google Persistent Disk on your server.
- CIncrease your network bandwidth from Compute Engine to Cloud Storage.
- DIncrease your network bandwidth from your datacenter to GCP.
87You have 15 TB of data in your on-premises data center that you want to transfer to Google Cloud. Your data changes weekly and is stored in a POSIX-compliant source. The network operations team has granted you 500 Mbps bandwidth to the public internet. You want to follow Google-recommended practices to reliably transfer your data to Google Cloud on a weekly basis. What should you do?
- AInstall Storage Transfer Service for on-premises data in your data center, and then configure a weekly transfer job.
- BUse Transfer Appliance to migrate your data into a Google Kubernetes Engine cluster, and then configure a weekly transfer job.
- CUse Cloud Scheduler to trigger the gsutil command. Use the -m parameter for optimal parallelism.
- DInstall Storage Transfer Service for on-premises data on a Google Cloud virtual machine, and then configure a weekly transfer job.
88You are on the data governance team and are implementing security requirements. You need to encrypt all your data in BigQuery by using an encryption key managed by your team. You must implement a mechanism to generate and store encryption material only on your on-premises hardware security module (HSM). You want to rely on Google managed solutions. What should you do?
- ACreate the encryption key in the on-premises HSM. Create BigQuery resources and encrypt data while ingesting them into BigQuery.
- BCreate the encryption key in the on-premises HSM, and import it into Cloud Key Management Service (Cloud HSM) key. Associate the created Cloud HSM key while creating the BigQuery resources.
- CCreate the encryption key in the on-premises HSM, and import it into a Cloud Key Management Service (Cloud KMS) key. Associate the created Cloud KMS key while creating the BigQuery resources.
- DCreate the encryption key in the on-premises HSM and link it to a Cloud External Key Manager (Cloud EKM) key. Associate the created Cloud KMS key while creating the BigQuery resources.
89You have one BigQuery dataset which includes customers’ street addresses. You want to retrieve all occurrences of street addresses from the dataset. What should you do?
- AWrite a SQL query in BigQuery by using REGEXP_CONTAINS on all tables in your dataset to find rows where the word “street” appears.
- BCreate a discovery scan configuration on your organization with Cloud Data Loss Prevention and create an inspection template that includes the STREET_ADDRESS infoType.
- CCreate a de-identification job in Cloud Data Loss Prevention and use the masking transformation.
- DCreate a deep inspection job on each table in your dataset with Cloud Data Loss Prevention and create an inspection template that includes the STREET_ADDRESS infoType.
90You are designing a data mesh on Google Cloud by using Dataplex to manage data in BigQuery and Cloud Storage. You want to simplify data asset permissions. You are creating a customer virtual lake with two user groups:
• Data engineers, which require full data lake access
• Analytic users, which require access to curated data
You need to assign access rights to these two groups. What should you do?- A1. Grant the bigquery.dataOwner role on BigQuery datasets and the storage.objectCreator role on Cloud Storage buckets to data engineers.
2. Grant the bigquery.dataViewer role on BigQuery datasets and the storage.objectViewer role on Cloud Storage buckets to analytic users. - B1. Grant the dataplex.dataReader role to the data engineer group on the customer data lake.
2. Grant the dataplex.dataOwner to the analytic user group on the customer curated zone. - C1. Grant the dataplex.dataOwner role to the data engineer group on the customer data lake.
2. Grant the dataplex.dataReader role to the analytic user group on the customer curated zone. - D1. Grant the bigquery.dataViewer role on BigQuery datasets and the storage.objectViewer role on Cloud Storage buckets to data engineers.
2. Grant the bigquery.dataOwner role on BigQuery datasets and the storage.objectEditor role on Cloud Storage buckets to analytic users.
- A1. Grant the bigquery.dataOwner role on BigQuery datasets and the storage.objectCreator role on Cloud Storage buckets to data engineers.
91You are developing an Apache Beam pipeline to extract data from a Cloud SQL instance by using JdbcIO. You have two projects running in Google Cloud. The pipeline will be deployed and executed on Dataflow in Project A. The Cloud SQL. instance is running in Project B and does not have a public IP address. After deploying the pipeline, you noticed that the pipeline failed to extract data from the Cloud SQL instance due to connection failure. You verified that VPC Service Controls and shared VPC are not in use in these projects. You want to resolve this error while ensuring that the data does not go through the public internet. What should you do?
- ASet up VPC Network Peering between Project A and Project B. Create a Compute Engine instance without external IP address in Project B on the peered subnet to serve as a proxy server to the Cloud SQL database.
- BTurn off the external IP addresses on the Dataflow worker. Enable Cloud NAT in Project A.
- CAdd the external IP addresses of the Dataflow worker as authorized networks in the Cloud SQL instance.
- DSet up VPC Network Peering between Project A and Project B. Add a firewall rule to allow the peered subnet range to access all instances on the network.
92You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?
- ACreate separate views to cover each month, and query from these views
- BEnable query caching so you can cache data from previous months
- CConvert the sharded tables into a single partitioned table
- DConvert all daily log tables into date-partitioned tables
93You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally.
You also want to optimize data for range queries on non-key columns. What should you do?- AUse Cloud SQL for storage. Add secondary indexes to support query patterns.
- BUse Cloud Spanner for storage. Add secondary indexes to support query patterns.
- CUse Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.
- DUse Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.
94Flowlogistic Case Study -
Company Overview -
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background -
The company started as a regional trucking company, and then expanded into other logistics market. Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept -
Flowlogistic wants to implement two concepts using the cloud:
✑ Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads
✑ Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment -
Flowlogistic architecture resides in a single data center:
✑ Databases
8 physical servers in 2 clusters
- SQL Server `" user data, inventory, static data
3 physical servers
- Cassandra `" metadata, tracking messages
10 Kafka servers `" tracking message aggregation and batch insert
✑ Application servers `" customer front end, middleware for order/customs
60 virtual machines across 20 physical servers
- Tomcat `" Java services
- Nginx `" static content
- Batch servers
✑ Storage appliances
- iSCSI for virtual machine (VM) hosts
- Fibre Channel storage area network (FC SAN) `" SQL server storage
- Network-attached storage (NAS) image storage, logs, backups
✑ 10 Apache Hadoop /Spark servers
- Core Data Lake
- Data analysis workloads
✑ 20 miscellaneous servers
- Jenkins, monitoring, bastion hosts,
Business Requirements -
Build a reliable and reproducible environment with scaled panty of production.
Question
✑ Aggregate data in a centralized Data Lake for analysis
✑ Use historical data to perform predictive analytics on future shipments
✑ Accurately track every shipment worldwide using proprietary technology
✑ Improve business agility and speed of innovation through rapid provisioning of new resources
✑ Analyze and optimize architecture for performance in the cloud
✑ Migrate fully to the cloud if all other requirements are met
Technical Requirements -
✑ Handle both streaming and batch data
✑ Migrate existing Hadoop workloads
✑ Ensure architecture is scalable and elastic to meet the changing demands of the company.
✑ Use managed services whenever possible
✑ Encrypt data flight and at rest
✑ Connect a VPN between the production data center and cloud environment
SEO Statement -
We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.
We need to organize our information so we can more easily understand where our customers are and what they are shipping.
CTO Statement -
IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.
CFO Statement -
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment.
Flowlogistic wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to
BigQuery. Flowlogistic does not know how to store the data that is common to both workloads. What should they do?- AStore the common data encoded as Avro in Google Cloud Storage.
- BStore the common data in BigQuery as partitioned tables.
- CStore he common data in the HDFS storage for a Google Cloud Dataproc cluster.
- DStore the common data in BigQuery and expose authorized views.
95Your company is streaming real-time sensor data from their factory floor into Bigtable and they have noticed extremely poor performance. How should the row key be redesigned to improve Bigtable performance on queries that populate real-time dashboards?
- AUse a row key of the form <sensorid>.
- BUse a row key of the form <timestamp>#<sensorid>.
- CUse a row key of the form >#<sensorid>#<timestamp>.
- DUse a row key of the form <timestamp>.
96You use BigQuery as your centralized analytics platform. New data is loaded every day, and an ETL pipeline modifies the original data and prepares it for the final users. This ETL pipeline is regularly modified and can generate errors, but sometimes the errors are detected only after 2 weeks. You need to provide a method to recover from these errors, and your backups should be optimized for storage costs. How should you organize your data in BigQuery and store your backups?
- AOrganize your data in a single table, export, and compress and store the BigQuery data in Cloud Storage.
- BOrganize your data in separate tables for each month, and export, compress, and store the data in Cloud Storage.
- COrganize your data in separate tables for each month, and use snapshot decorators to restore the table to a time prior to the corruption.
- DOrganize your data in separate tables for each month, and duplicate your data on a separate dataset in BigQuery.
97You have a data processing application that runs on Google Kubernetes Engine (GKE). Containers need to be launched with their latest available configurations from a container registry. Your GKE nodes need to have GPUs, local SSDs, and 8 Gbps bandwidth. You want to efficiently provision the data processing infrastructure and manage the deployment process. What should you do?
- AUse Cloud Build to schedule a job using Terraform build to provision the infrastructure and launch with the most current container images.
- BUse Dataflow to provision the data pipeline, and use Cloud Scheduler to run the job.
- CUse Compute Engine startup scripts to pull container images, and use gcloud commands to provision the infrastructure.
- DUse GKE to autoscale containers, and use gcloud commands to provision the infrastructure.
98Your financial services company is moving to cloud technology and wants to store 50 TB of financial time-series data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data.
Which product should they use to store the data?- ACloud Bigtable
- BGoogle Cloud Datastore
- CGoogle BigQuery
- DGoogle Cloud Storage
99You want to migrate an Apache Spark 3 batch job from on-premises to Google Cloud. You need to minimally change the job so that the job reads from Cloud Storage and writes the result to BigQuery. Your job is optimized for Spark, where each executor has 8 vCPU and 16 GB memory, and you want to be able to choose similar settings. You want to minimize installation and management effort to run your job. What should you do?
- AExecute the job from a new Compute Engine VM.
- BExecute as a Dataproc Serverless job.
- CExecute the job as part of a deployment in a new Google Kubernetes Engine cluster.
- DExecute the job in a new Dataproc cluster.
100You are using a Dataflow streaming job to read messages from a message bus that does not support exactly-once delivery. Your job then applies some transformations, and loads the result into BigQuery. You want to ensure that your data is being streamed into BigQuery with exactly-once delivery semantics. You expect your ingestion throughput into BigQuery to be about 1.5 GB per second. What should you do?
- AUse the BigQuery Storage Write API and ensure that your target BigQuery table is multiregional.
- BUse the BigQuery Streaming API and ensure that your target BigQuery table is regional.
- CUse the BigQuery Storage Write API and ensure that your target BigQuery table is regional.
- DUse the BigQuery Streaming API and ensure that your target BigQuery table is multiregional.
101You work for a financial institution that lets customers register online. As new customers register, their user data is sent to Pub/Sub before being ingested into
BigQuery. For security reasons, you decide to redact your customers' Government issued Identification Number while allowing customer service representatives to view the original values when necessary. What should you do?- AUse BigQuery column-level security. Set the table permissions so that only members of the Customer Service user group can see the SSN column.
- BUse BigQuery's built-in AEAD encryption to encrypt the SSN column. Save the keys to a new table that is only viewable by permissioned users.
- CBefore loading the data into BigQuery, use Cloud Data Loss Prevention (DLP) to replace input values with a cryptographic format-preserving encryption token.
- DBefore loading the data into BigQuery, use Cloud Data Loss Prevention (DLP) to replace input values with a cryptographic hash.
102You currently have a single on-premises Kafka cluster in a data center in the us-east region that is responsible for ingesting messages from IoT devices globally.
Because large parts of globe have poor internet connectivity, messages sometimes batch at the edge, come in all at once, and cause a spike in load on your
Kafka cluster. This is becoming difficult to manage and prohibitively expensive. What is the Google-recommended cloud native architecture for this scenario?- AAn IoT gateway connected to Cloud Pub/Sub, with Cloud Dataflow to read and process the messages from Cloud Pub/Sub.
- BA Kafka cluster virtualized on Compute Engine in us-east with Cloud Load Balancing to connect to the devices around the world.
- CCloud Dataflow connected to the Kafka cluster to scale the processing of incoming messages.
- DEdge TPUs as sensor devices for storing and transmitting the messages.
103You work for an airline and you need to store weather data in a BigQuery table. Weather data will be used as input to a machine learning model. The model only uses the last 30 days of weather data. You want to avoid storing unnecessary data and minimize costs. What should you do?
- ACreate a BigQuery table partitioned by datetime value of the weather date. Set up partition expiration to 30 days.
- BCreate a BigQuery table partitioned by ingestion time. Set up partition expiration to 30 days.
- CCreate a BigQuery table where each record has an ingestion timestamp. Run a scheduled query to delete all the rows with an ingestion timestamp older than 30 days.
- DCreate a BigQuery table with a datetime column for the day the weather data refers to. Run a scheduled query to delete rows with a datetime value older than 30 days.
104You are testing a Dataflow pipeline to ingest and transform text files. The files are compressed gzip, errors are written to a dead-letter queue, and you are using
SideInputs to join data. You noticed that the pipeline is taking longer to complete than expected; what should you do to expedite the Dataflow job?- ARetry records that throw an error.
- BUse CoGroupByKey instead of the SideInput.
- CReduce the batch size.
- DSwitch to compressed Avro files.
105You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?
- APigLatin using Pig
- BJava using MapReduce
- CHiveQL using Hive
- DPython using MapReduce
106Your organization's data assets are stored in BigQuery, Pub/Sub, and a PostgreSQL instance running on Compute Engine. Because there are multiple domains and diverse teams using the data, teams in your organization are unable to discover existing data assets. You need to design a solution to improve data discoverability while keeping development and configuration efforts to a minimum. What should you do?
- AUse Data Catalog to automatically catalog BigQuery datasets. Use Data Catalog APIs to manually catalog Pub/Sub topics and PostgreSQL tables.
- BUse Data Catalog to automatically catalog BigQuery datasets and Pub/Sub topics. Use custom connectors to manually catalog PostgreSQL tables.
- CUse customer connectors to manually catalog BigQuery datasets, Pub/Sub topics, and PostgreSQL tables.
- DUse Data Catalog to automatically catalog BigQuery datasets and Pub/Sub topics. Use Data Catalog APIs to manually catalog PostgreSQL tables.
107The Development and External teams have the project viewer Identity and Access Management (IAM) role in a folder named Visualization. You want the
Development Team to be able to read data from both Cloud Storage and BigQuery, but the External Team should only be able to read data from BigQuery. What should you do?
- ARemove Cloud Storage IAM permissions to the External Team on the acme-raw-data project.
- BCreate a VPC Service Controls perimeter containing both projects and Cloud Storage as a restricted API. Add the Development Team users to the perimeter's Access Level.
- CCreate Virtual Private Cloud (VPC) firewall rules on the acme-raw-data project that deny all ingress traffic from the External Team CIDR range.
- DCreate a VPC Service Controls perimeter containing both projects and BigQuery as a restricted API. Add the External Team users to the perimeter's Access Level.
108You are implementing workflow pipeline scheduling using open source-based tools and Google Kubernetes Engine (GKE). You want to use a Google managed service to simplify and automate the task. You also want to accommodate Shared VPC networking considerations. What should you do?
- AUse Dataflow for your workflow pipelines. Use shell scripts to schedule workflows.
- BUse Cloud Composer in a Shared VPC configuration. Place the Cloud Composer resources in the host project.
- CUse Dataflow for your workflow pipelines. Use Cloud Run triggers for scheduling.
- DUse Cloud Composer in a Shared VPC configuration. Place the Cloud Composer resources in the service project.
109Your company needs to upload their historic data to Cloud Storage. The security rules don't allow access from external IPs to their on-premises resources. After an initial upload, they will add new data from existing on-premises applications every day. What should they do?
- AUse Dataflow and write the data to Cloud Storage.
- BInstall an FTP server on a Compute Engine VM to receive the files and move them to Cloud Storage.
- CWrite a job template in Dataproc to perform the data transfer.
- DExecute gsutil rsync from the on-premises servers.
110You have a streaming pipeline that ingests data from Pub/Sub in production. You need to update this streaming pipeline with improved business logic. You need to ensure that the updated pipeline reprocesses the previous two days of delivered Pub/Sub messages. What should you do? (Choose two.)
- AUse Pub/Sub Snapshot capture two days before the deployment.
- BUse the Pub/Sub subscription retain-acked-messages flag.
- CUse Pub/Sub Seek with a timestamp.
- DCreate a new Pub/Sub subscription two days before the deployment.
- EUse the Pub/Sub subscription clear-retry-policy flag
111You created a new version of a Dataflow streaming data ingestion pipeline that reads from Pub/Sub and writes to BigQuery. The previous version of the pipeline that runs in production uses a 5-minute window for processing. You need to deploy the new version of the pipeline without losing any data, creating inconsistencies, or increasing the processing latency by more than 10 minutes. What should you do?
- ACancel the old pipeline, then start the new pipeline.
- BUpdate the old pipeline with the new pipeline code.
- CDrain the old pipeline, then start the new pipeline.
- DSnapshot the old pipeline, stop the old pipeline, and then start the new pipeline from the snapshot.
112An organization maintains a Google BigQuery dataset that contains tables with user-level data. They want to expose aggregates of this data to other Google
Cloud projects, while still controlling access to the user-level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects. What should they do?- ACreate and share an authorized view that provides the aggregate results.
- BCreate and share a new dataset and table that contains the aggregate results.
- CCreate dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.
- DCreate and share a new dataset and view that provides the aggregate results.
113You have several different unstructured data sources, within your on-premises data center as well as in the cloud. The data is in various formats, such as Apache Parquet and CSV. You want to centralize this data in Cloud Storage. You need to set up an object sink for your data that allows you to use your own encryption keys. You want to use a GUI-based solution. What should you do?
- AUse Dataflow to move files into Cloud Storage
- BUse Cloud Data Fusion to move files into Cloud Storage.
- CUse Storage Transfer Service to move files into Cloud Storage
- DUse BigQuery Data Transfer Service to move files into BigQuery.
114You are migrating an application that tracks library books and information about each book, such as author or year published, from an on-premises data warehouse to BigQuery. In your current relational database, the author information is kept in a separate table and joined to the book information on a common key. Based on Google's recommended practice for schema design, how would you structure the data to ensure optimal speed of queries about the author of each book that has been borrowed?
- ACreate a table that is wide and includes a column for each attribute, including the author's first name, last name, date of birth, etc.
- BKeep the schema the same, create a view that joins all of the tables, and always query the view.
- CKeep the schema the same, maintain the different tables for the book and each of the attributes, and query as you are doing today.
- DCreate a table that includes information about the books and authors, but nest the author fields inside the author column.
115You have a data pipeline with a Dataflow job that aggregates and writes time series metrics to Bigtable. You notice that data is slow to update in Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the data. Which two actions should you take? (Choose two.)
- AIncrease the number of nodes in the Bigtable cluster
- BConfigure your Dataflow pipeline to use local execution
- CModify your Dataflow pipeline to use the CoGroupByKey transform before writing to Bigtable
- DModify your Dataflow pipeline to use the Flatten transform before writing to Bigtable
- EIncrease the maximum number of Dataflow workers by setting maxNumWorkers in PipelineOptions
116The data analyst team at your company uses BigQuery for ad-hoc queries and scheduled SQL pipelines in a Google Cloud project with a slot reservation of 2000 slots. However, with the recent introduction of hundreds of new non time-sensitive SQL pipelines, the team is encountering frequent quota errors. You examine the logs and notice that approximately 1500 queries are being triggered concurrently during peak time. You need to resolve the concurrency issue. What should you do?
- AUpdate SQL pipelines and ad-hoc queries to run as interactive query jobs.
- BUpdate SQL pipelines to run as a batch query, and run ad-hoc queries as interactive query jobs.
- CIncrease the slot capacity of the project with baseline as 0 and maximum reservation size as 3000.
- DIncrease the slot capacity of the project with baseline as 2000 and maximum reservation size as 3000.
117You set up a streaming data insert into a Redis cluster via a Kafka cluster. Both clusters are running on Compute Engine instances. You need to encrypt data at rest with encryption keys that you can create, rotate, and destroy as needed. What should you do?
- ACreate encryption keys in Cloud Key Management Service. Reference those keys in your API service calls when accessing the data in your Compute Engine cluster instances.
- BCreate encryption keys in Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
- CCreate encryption keys locally. Upload your encryption keys to Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
- DCreate a dedicated service account, and use encryption at rest to reference your data stored in your Compute Engine cluster instances as part of your API service calls.
118You are designing a messaging system by using Pub/Sub to process clickstream data with an event-driven consumer app that relies on a push subscription. You need to configure the messaging system that is reliable enough to handle temporary downtime of the consumer app. You also need the messaging system to store the input messages that cannot be consumed by the subscriber. The system needs to retry failed messages gradually, avoiding overloading the consumer app, and store the failed messages after a maximum of 10 retries in a topic. How should you configure the Pub/Sub subscription?
- AUse exponential backoff as the subscription retry policy, and configure dead lettering to a different topic with maximum delivery attempts set to 10.
- BIncrease the acknowledgement deadline to 10 minutes.
- CUse exponential backoff as the subscription retry policy, and configure dead lettering to the same source topic with maximum delivery attempts set to 10.
- DUse immediate redelivery as the subscription retry policy, and configure dead lettering to a different topic with maximum delivery attempts set to 10.
119Your organization stores customer data in an on-premises Apache Hadoop cluster in Apache Parquet format. Data is processed on a daily basis by Apache Spark jobs that run on the cluster. You are migrating the Spark jobs and Parquet data to Google Cloud. BigQuery will be used on future transformation pipelines so you need to ensure that your data is available in BigQuery. You want to use managed services, while minimizing ETL data processing changes and overhead costs. What should you do?
- AMigrate your data to BigLake. Refactor Spark pipelines to write and read data on Cloud Storage, and run them on Dataproc on Compute Engine.
- BMigrate your data to Cloud Storage and migrate the metadata to Dataproc Metastore (DPMS). Refactor Spark pipelines to write and read data on Cloud Storage, and run them on Dataproc Serverless.
- CMigrate your data to BigQuery. Refactor Spark pipelines to write and read data on BigQuery, and run them on Dataproc Serverless.
- DMigrate your data to Cloud Storage and register the bucket as a Dataplex asset. Refactor Spark pipelines to write and read data on Cloud Storage, and run them on Dataproc Serverless.
120You have thousands of Apache Spark jobs running in your on-premises Apache Hadoop cluster. You want to migrate the jobs to Google Cloud. You want to use managed services to run your jobs instead of maintaining a long-lived Hadoop cluster yourself. You have a tight timeline and want to keep code changes to a minimum. What should you do?
- AMove your data to Cloud Storage. Run your jobs on Dataproc.
- BCopy your data to Compute Engine disks. Manage and run your jobs directly on those instances.
- CRewrite your jobs in Apache Beam. Run your jobs in Dataflow.
- DMove your data to BigQuery. Convert your Spark scripts to a SQL-based processing approach.
121You work on a regression problem in a natural language processing domain, and you have 100M labeled examples in your dataset. You have randomly shuffled your data and split your dataset into train and test samples (in a 90/10 ratio). After you trained the neural network and evaluated your model on a test set, you discover that the root-mean-squared error (RMSE) of your model is twice as high on the train set as on the test set. How should you improve the performance of your model?
- AIncrease the complexity of your model by, e.g., introducing an additional layer or increase sizing the size of vocabularies or n-grams used.
- BIncrease the share of the test sample in the train-test split.
- CTry out regularization techniques (e.g., dropout of batch normalization) to avoid overfitting.
- DTry to collect more data and increase the size of your dataset.
122You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old. What should you do?
- ADisable caching by editing the report settings.
- BClear your browser history for the past hour then reload the tab showing the virtualizations.
- CDisable caching in BigQuery by editing table details.
- DRefresh your browser tab showing the visualizations.
123Your infrastructure includes a set of YouTube channels. You have been tasked with creating a process for sending the YouTube channel data to Google Cloud for analysis. You want to design a solution that allows your world-wide marketing teams to perform ANSI SQL and other types of analysis on up-to-date YouTube channels log data. How should you set up the log data transfer into Google Cloud?
- AUse Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Regional bucket as a final destination.
- BUse Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.
- CUse BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.
- DUse BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Regional storage bucket as a final destination.
124You are a BigQuery admin supporting a team of data consumers who run ad hoc queries and downstream reporting in tools such as Looker. All data and users are combined under a single organizational project. You recently noticed some slowness in query results and want to troubleshoot where the slowdowns are occurring. You think that there might be some job queuing or slot contention occurring as users run jobs, which slows down access to results. You need to investigate the query job information and determine where performance is being affected. What should you do?
- AUse available administrative resource charts to determine how slots are being used and how jobs are performing over time. Run a query on the INFORMATION_SCHEMA to review query performance.
- BUse slot reservations for your project to ensure that you have enough query processing capacity and are able to allocate available slots to the slower queries.
- CUse Cloud Monitoring to view BigQuery metrics and set up alerts that let you know when a certain percentage of slots were used.
- DUse Cloud Logging to determine if any users or downstream consumers are changing or deleting access grants on tagged resources.
125You have a Standard Tier Memorystore for Redis instance deployed in a production environment. You need to simulate a Redis instance failover in the most accurate disaster recovery situation, and ensure that the failover has no impact on production data. What should you do?
- AIncrease one replica to Redis instance in production environment. Initiate a manual failover by using the force-data-loss data protection mode.
- BCreate a Standard Tier Memorystore for Redis instance in the development environment. Initiate a manual failover by using the limited-data-loss data protection mode.
- CInitiate a manual failover by using the limited-data-loss data protection mode to the Memorystore for Redis instance in the production environment.
- DCreate a Standard Tier Memorystore for Redis instance in a development environment. Initiate a manual failover by using the force-data-loss data protection mode.
126You have a requirement to insert minute-resolution data from 50,000 sensors into a BigQuery table. You expect significant growth in data volume and need the data to be available within 1 minute of ingestion for real-time analysis of aggregated trends. What should you do?
- AUse the INSERT statement to insert a batch of data every 60 seconds.
- BUse a Cloud Dataflow pipeline to stream data into the BigQuery table.
- CUse bq load to load a batch of sensor data every 60 seconds.
- DUse the MERGE statement to apply updates in batch every 60 seconds.
127Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?
- AReduce the query frequency to once every hour until the database comes back online.
- BIssue a command to restart the database servers.
- CRetry the query every second until it comes back online to minimize staleness of data.
- DRetry the query with exponential backoff, up to a cap of 15 minutes.
128Your startup has a web application that currently serves customers out of a single region in Asia. You are targeting funding that will allow your startup to serve customers globally. Your current goal is to optimize for cost, and your post-funding goal is to optimize for global presence and performance. You must use a native
JDBC driver. What should you do?- AUse a Cloud SQL for PostgreSQL zonal instance first, and Bigtable with US, Europe, and Asia after securing funding.
- BUse Cloud Spanner to configure a single region instance initially, and then configure multi-region Cloud Spanner instances after securing funding.
- CUse a Cloud SQL for PostgreSQL highly available instance first, and Bigtable with US, Europe, and Asia replication after securing funding.
- DUse a Cloud SQL for PostgreSQL zonal instance first, and Cloud SQL for PostgreSQL with highly available configuration after securing funding.
129You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application. What should you do?
- ACreate groups for your users and give those groups access to the dataset
- BCreate a dummy user and grant dataset access to that user. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
- CIntegrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
- DCreate a service account and grant dataset access to that account. Use the service account's private key to access the dataset
130Flowlogistic Case Study -
Company Overview -
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background -
The company started as a regional trucking company, and then expanded into other logistics market. Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept -
Flowlogistic wants to implement two concepts using the cloud:
✑ Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads
✑ Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment -
Flowlogistic architecture resides in a single data center:
✑ Databases
- 8 physical servers in 2 clusters
- SQL Server `" user data, inventory, static data
- 3 physical servers
- Cassandra `" metadata, tracking messages
10 Kafka servers `" tracking message aggregation and batch insert
✑ Application servers `" customer front end, middleware for order/customs
- 60 virtual machines across 20 physical servers
- Tomcat `" Java services
- Nginx `" static content
- Batch servers
✑ Storage appliances
- iSCSI for virtual machine (VM) hosts
- Fibre Channel storage area network (FC SAN) `" SQL server storage
Network-attached storage (NAS) image storage, logs, backups
✑ 10 Apache Hadoop /Spark servers
- Core Data Lake
- Data analysis workloads
✑ 20 miscellaneous servers
- Jenkins, monitoring, bastion hosts,
Business Requirements -
✑ Build a reliable and reproducible environment with scaled panty of production.
✑ Aggregate data in a centralized Data Lake for analysis
✑ Use historical data to perform predictive analytics on future shipments
✑ Accurately track every shipment worldwide using proprietary technology
✑ Improve business agility and speed of innovation through rapid provisioning of new resources
✑ Analyze and optimize architecture for performance in the cloud
✑ Migrate fully to the cloud if all other requirements are met
Technical Requirements -
✑ Handle both streaming and batch data
✑ Migrate existing Hadoop workloads
✑ Ensure architecture is scalable and elastic to meet the changing demands of the company.
✑ Use managed services whenever possible
✑ Encrypt data flight and at rest
Connect a VPN between the production data center and cloud environment
SEO Statement -
We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.
We need to organize our information so we can more easily understand where our customers are and what they are shipping.
CTO Statement -
IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.
CFO Statement -
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment.
Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system.
You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?- ACloud Dataflow, Cloud SQL, and Cloud Storage
- BCloud Load Balancing, Cloud Dataflow, and Cloud Storage
- CCloud Pub/Sub, Cloud Dataflow, and Cloud Storage
- DCloud Pub/Sub, Cloud SQL, and Cloud Storage
- ECloud Pub/Sub, Cloud Dataflow, and Local SSD
131You are troubleshooting your Dataflow pipeline that processes data from Cloud Storage to BigQuery. You have discovered that the Dataflow worker nodes cannot communicate with one another. Your networking team relies on Google Cloud network tags to define firewall rules. You need to identify the issue while following Google-recommended networking security practices. What should you do?
- ADetermine whether there is a firewall rule set to allow traffic on TCP ports 12345 and 12346 on the subnet used by Dataflow workers.
- BDetermine whether your Dataflow pipeline has a custom network tag set.
- CDetermine whether there is a firewall rule set to allow traffic on TCP ports 12345 and 12346 for the Dataflow network tag.
- DDetermine whether your Dataflow pipeline is deployed with the external IP address option enabled.
132You are creating a data model in BigQuery that will hold retail transaction data. Your two largest tables, sales_transaction_header and sales_transaction_line, have a tightly coupled immutable relationship. These tables are rarely modified after load and are frequently joined when queried. You need to model the sales_transaction_header and sales_transaction_line tables to improve the performance of data analytics queries. What should you do?
- ACreate a sales_transaction table that holds the sales_transaction_header and sales_transaction_line information as rows, duplicating the sales_transaction_header data for each line.
- BCreate a sales_transaction table that holds the sales_transaction_header information as rows and the sales_transaction_line rows as nested and repeated fields.
- CCreate a sales_transaction table that stores the sales_transaction_header and sales_transaction_line data as a JSON data type.
- DCreate separate sales_transaction_header and sales_transaction_line tables and, when querying, specify the sales_transaction_line first in the WHERE clause.
133You have designed an Apache Beam processing pipeline that reads from a Pub/Sub topic. The topic has a message retention duration of one day, and writes to a Cloud Storage bucket. You need to select a bucket location and processing strategy to prevent data loss in case of a regional outage with an RPO of 15 minutes. What should you do?
- A1. Use a multi-regional Cloud Storage bucket.
2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs.
3. Seek the subscription back in time by 60 minutes to recover the acknowledged messages.
4. Start the Dataflow job in a secondary region. - B1. Use a dual-region Cloud Storage bucket with turbo replication enabled.
2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs.
3. Seek the subscription back in time by 60 minutes to recover the acknowledged messages.
4. Start the Dataflow job in a secondary region. - C1. Use a dual-region Cloud Storage bucket.
2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs.
3. Seek the subscription back in time by 15 minutes to recover the acknowledged messages.
4. Start the Dataflow job in a secondary region. - D1. Use a regional Cloud Storage bucket.
2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs.
3. Seek the subscription back in time by one day to recover the acknowledged messages.
4. Start the Dataflow job in a secondary region and write in a bucket in the same region.
- A1. Use a multi-regional Cloud Storage bucket.
134You have several different file type data sources, such as Apache Parquet and CSV. You want to store the data in Cloud Storage. You need to set up an object sink for your data that allows you to use your own encryption keys. You want to use a GUI-based solution. What should you do?
- AUse Storage Transfer Service to move files into Cloud Storage.
- BUse BigQuery Data Transfer Service to move files into BigQuery.
- CUse Dataflow to move files into Cloud Storage.
- DUse Cloud Data Fusion to move files into Cloud Storage.
135You are designing a pipeline that publishes application events to a Pub/Sub topic. Although message ordering is not important, you need to be able to aggregate events across disjoint hourly intervals before loading the results to BigQuery for analysis. What technology should you use to process and load this data to
BigQuery while ensuring that it will scale with large volumes of events?- ASchedule a batch Dataflow job to run hourly, pulling all available messages from the Pub/Sub topic and performing the necessary aggregations.
- BCreate a Cloud Function to perform the necessary data processing that executes using the Pub/Sub trigger every time a new message is published to the topic.
- CSchedule a Cloud Function to run hourly, pulling all available messages from the Pub/Sub topic and performing the necessary aggregations.
- DCreate a streaming Dataflow job that reads continually from the Pub/Sub topic and performs the necessary aggregations using tumbling windows.
136Your new customer has requested daily reports that show their net consumption of Google Cloud compute resources and who used the resources. You need to quickly and efficiently generate these daily reports. What should you do?
- AFilter data in Cloud Logging by project, resource, and user; then export the data in CSV format.
- BDo daily exports of Cloud Logging data to BigQuery. Create views filtering by project, log type, resource, and user.
- CExport Cloud Logging data to Cloud Storage in CSV format. Cleanse the data using Dataprep, filtering by project, resource, and user.
- DFilter data in Cloud Logging by project, log type, resource, and user, then import the data into BigQuery.
137You are building an ELT solution in BigQuery by using Dataform. You need to perform uniqueness and null value checks on your final tables. What should you do to efficiently integrate these checks into your pipeline?
- ABuild BigQuery user-defined functions (UDFs).
- BBuild Dataform assertions into your code.
- CWrite a Spark-based stored procedure.
- DCreate Dataplex data quality tasks.
138You want to rebuild your batch pipeline for structured data on Google Cloud. You are using PySpark to conduct data transformations at scale, but your pipelines are taking over twelve hours to run. To expedite development and pipeline run time, you want to use a serverless tool and SOL syntax. You have already moved your raw data into Cloud Storage. How should you build the pipeline on Google Cloud while meeting speed and processing requirements?
- AUse Apache Beam Python SDK to build the transformation pipelines, and write the data into BigQuery.
- BIngest your data into Cloud SQL, convert your PySpark commands into SparkSQL queries to transform the data, and then use federated quenes from BigQuery for machine learning.
- CIngest your data into BigQuery from Cloud Storage, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table.
- DConvert your PySpark commands into SparkSQL queries to transform the data, and then run your pipeline on Dataproc to write the data into BigQuery.
139You have a data pipeline that writes data to Cloud Bigtable using well-designed row keys. You want to monitor your pipeline to determine when to increase the size of your Cloud Bigtable cluster. Which two actions can you take to accomplish this? (Choose two.)
- AReview Key Visualizer metrics. Increase the size of the Cloud Bigtable cluster when the Read pressure index is above 100.
- BMonitor the latency of write operations. Increase the size of the Cloud Bigtable cluster when there is a sustained increase in write latency.
- CMonitor storage utilization. Increase the size of the Cloud Bigtable cluster when utilization increases above 70% of max capacity.
- DMonitor latency of read operations. Increase the size of the Cloud Bigtable cluster of read operations take longer than 100 ms.
- EReview Key Visualizer metrics. Increase the size of the Cloud Bigtable cluster when the Write pressure index is above 100.
140You need to create a near real-time inventory dashboard that reads the main inventory tables in your BigQuery data warehouse. Historical inventory data is stored as inventory balances by item and location. You have several thousand updates to inventory every hour. You want to maximize performance of the dashboard and ensure that the data is accurate. What should you do?
- AUse the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
- BLeverage BigQuery UPDATE statements to update the inventory balances as they are changing.
- CUse the BigQuery streaming the stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
- DPartition the inventory balance table by item to reduce the amount of data scanned with each inventory update.
141You are loading CSV files from Cloud Storage to BigQuery. The files have known data quality issues, including mismatched data types, such as STRINGs and
INT64s in the same column, and inconsistent formatting of values such as phone numbers or addresses. You need to create the data pipeline to maintain data quality and perform the required cleansing and transformation. What should you do?- AUse Data Fusion to convert the CSV files to a self-describing data format, such as AVRO, before loading the data to BigQuery.
- BCreate a table with the desired schema, load the CSV files into the table, and perform the transformations in place using SQL.
- CUse Data Fusion to transform the data before loading it into BigQuery.
- DLoad the CSV files into a staging table with the desired schema, perform the transformations with SQL, and then write the results to the final destination table.
142Your software uses a simple JSON format for all messages. These messages are published to Google Cloud Pub/Sub, then processed with Google Cloud
Dataflow to create a real-time dashboard for the CFO. During testing, you notice that some messages are missing in the dashboard. You check the logs, and all messages are being published to Cloud Pub/Sub successfully. What should you do next?- ACheck the dashboard application to see if it is not displaying correctly.
- BSwitch Cloud Dataflow to pull messages from Cloud Pub/Sub instead of Cloud Pub/Sub pushing messages to Cloud Dataflow.
- CRun a fixed dataset through the Cloud Dataflow pipeline and analyze the output.
- DUse Google Stackdriver Monitoring on Cloud Pub/Sub to find the missing messages.
143You work for a bank. You have a labelled dataset that contains information on already granted loan application and whether these applications have been defaulted. You have been asked to train a model to predict default rates for credit applicants.
What should you do?- ATrain a linear regression to predict a credit default risk score.
- BMatch loan applicants with their social profiles to enable feature engineering.
- CRemove the bias from the data and collect applications that have been declined loans.
- DIncrease the size of the dataset by collecting additional data.
144Your organization uses a multi-cloud data storage strategy, storing data in Cloud Storage, and data in Amazon Web Services’ (AWS) S3 storage buckets. All data resides in US regions. You want to query up-to-date data by using BigQuery, regardless of which cloud the data is stored in. You need to allow users to query the tables from BigQuery without giving direct access to the data in the storage buckets. What should you do?
- AUse the Storage Transfer Service to copy data from the AWS S3 buckets to Cloud Storage buckets. Create BigLake tables over the Cloud Storage data and query the data using BigQuery directly.
- BUse the Storage Transfer Service to copy data from the AWS S3 buckets to Cloud Storage buckets. Create external tables over the Cloud Storage data and query the data using BigQuery directly.
- CSetup a BigQuery Omni connection to the AWS S3 bucket data. Create BigLake tables over the Cloud Storage and S3 data and query the data using BigQuery directly.
- DSet up a BigQuery Omni connection to the AWS S3 bucket data. Create external tables over the Cloud Storage and S3 data and query the data using BigQuery directly.
145You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit tracking numbers when events are sent to Kafka topics. A recent software update caused the scanners to accidentally transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?
- ACreate an authorized view in BigQuery to restrict access to tables with sensitive data.
- BBuild a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention (Cloud DLP) API. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
- CInstall a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
- DUse Cloud Logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
146Your United States-based company has created an application for assessing and responding to user actions. The primary table's data volume grows by 250,000 records per second. Many third parties use your application's APIs to build the functionality into their own frontend applications. Your application's APIs should comply with the following requirements:
✑ Single global endpoint
✑ ANSI SQL support
✑ Consistent access to the most up-to-date data
What should you do?- AImplement BigQuery with no region selected for storage or processing.
- BImplement Bigtable with the primary cluster in North America and secondary clusters in Asia and Europe.
- CImplement Cloud Spanner with the leader in North America and read-only replicas in Asia and Europe.
- DImplement Cloud SQL for PostgreSQL with the master in North America and read replicas in Asia and Europe.
147You migrated your on-premises Apache Hadoop Distributed File System (HDFS) data lake to Cloud Storage. The data scientist team needs to process the data by using Apache Spark and SQL. Security policies need to be enforced at the column level. You need a cost-effective solution that can scale into a data mesh. What should you do?
- A1. Define a BigLake table.
2. Create a taxonomy of policy tags in Data Catalog.
3. Add policy tags to columns.
4. Process with the Spark-BigQuery connector or BigQuery SQL. - B1. Load the data to BigQuery tables.
2. Create a taxonomy of policy tags in Data Catalog.
3. Add policy tags to columns.
4. Process with the Spark-BigQuery connector or BigQuery SQL. - C1. Apply an Identity and Access Management (IAM) policy at the file level in Cloud Storage.
2. Define a BigQuery external table for SQL processing.
3. Use Dataproc Spark to process the Cloud Storage files. - D1. Deploy a long-living Dataproc cluster with Apache Hive and Ranger enabled.
2. Configure Ranger for column level security.
3. Process with Dataproc Spark or Hive SQL.
- A1. Define a BigLake table.
148You are managing a Dataplex environment with raw and curated zones. A data engineering team is uploading JSON and CSV files to a bucket asset in the curated zone but the files are not being automatically discovered by Dataplex. What should you do to ensure that the files are discovered by Dataplex?
- AMove the JSON and CSV files to the raw zone.
- BUse the bg command-line tool to load the JSON and CSV files into BigQuery tables.
- CGrant object level access to the CSV and JSON files in Cloud Storage.
- DEnable auto-discovery of files for the curated zone.
149Flowlogistic Case Study -
Company Overview -
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background -
The company started as a regional trucking company, and then expanded into other logistics market. Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept -
Flowlogistic wants to implement two concepts using the cloud:
✑ Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads
✑ Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment -
Flowlogistic architecture resides in a single data center:
✑ Databases
8 physical servers in 2 clusters
- SQL Server `" user data, inventory, static data
3 physical servers
- Cassandra `" metadata, tracking messages
10 Kafka servers `" tracking message aggregation and batch insert
✑ Application servers `" customer front end, middleware for order/customs
60 virtual machines across 20 physical servers
- Tomcat `" Java services
- Nginx `" static content
- Batch servers
✑ Storage appliances
- iSCSI for virtual machine (VM) hosts
- Fibre Channel storage area network (FC SAN) `" SQL server storage
- Network-attached storage (NAS) image storage, logs, backups
✑ 10 Apache Hadoop /Spark servers
- Core Data Lake
- Data analysis workloads
✑ 20 miscellaneous servers
- Jenkins, monitoring, bastion hosts,
Business Requirements -
✑ Build a reliable and reproducible environment with scaled panty of production.
✑ Aggregate data in a centralized Data Lake for analysis
✑ Use historical data to perform predictive analytics on future shipments
✑ Accurately track every shipment worldwide using proprietary technology
✑ Improve business agility and speed of innovation through rapid provisioning of new resources
✑ Analyze and optimize architecture for performance in the cloud
✑ Migrate fully to the cloud if all other requirements are met
Technical Requirements -
✑ Handle both streaming and batch data
✑ Migrate existing Hadoop workloads
✑ Ensure architecture is scalable and elastic to meet the changing demands of the company.
✑ Use managed services whenever possible
✑ Encrypt data flight and at rest
✑ Connect a VPN between the production data center and cloud environment
SEO Statement -
We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.
We need to organize our information so we can more easily understand where our customers are and what they are shipping.
CTO Statement -
IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.
CFO Statement -
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment.
Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single
Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in
Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.
Which approach should you take?- AUse the NOW () function in BigQuery to record the event's time.
- BUse the automatically generated timestamp from Cloud Pub/Sub to order the data.
- CAttach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.
- DAttach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.
150Your company currently runs a large on-premises cluster using Spark, Hive, and HDFS in a colocation facility. The cluster is designed to accommodate peak usage on the system; however, many jobs are batch in nature, and usage of the cluster fluctuates quite dramatically. Your company is eager to move to the cloud to reduce the overhead associated with on-premises infrastructure and maintenance and to benefit from the cost savings. They are also hoping to modernize their existing infrastructure to use more serverless offerings in order to take advantage of the cloud. Because of the timing of their contract renewal with the colocation facility, they have only 2 months for their initial migration. How would you recommend they approach their upcoming migration strategy so they can maximize their cost savings in the cloud while still executing the migration in time?
- AMigrate the workloads to Dataproc plus HDFS; modernize later.
- BMigrate the Spark workload to Dataproc plus HDFS, and modernize the Hive workload for BigQuery.
- CMigrate the workloads to Dataproc plus Cloud Storage; modernize later.
- DModernize the Spark workload for Dataflow and the Hive workload for BigQuery.
151You are deploying 10,000 new Internet of Things devices to collect temperature data in your warehouses globally. You need to process, store and analyze these very large datasets in real time. What should you do?
- ASend the data to Google Cloud Pub/Sub, stream Cloud Pub/Sub to Google Cloud Dataflow, and store the data in Google BigQuery.
- BSend the data to Google Cloud Datastore and then export to BigQuery.
- CExport logs in batch to Google Cloud Storage and then spin up a Google Cloud SQL instance, import the data from Cloud Storage, and run an analysis as needed.
- DSend the data to Cloud Storage and then spin up an Apache Hadoop cluster as needed in Google Cloud Dataproc whenever analysis is required.
152Flowlogistic Case Study -
Company Overview -
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background -
The company started as a regional trucking company, and then expanded into other logistics market. Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept -
Flowlogistic wants to implement two concepts using the cloud:
Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads
Question
✑ Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment -
Flowlogistic architecture resides in a single data center:
✑ Databases
8 physical servers in 2 clusters
- SQL Server `" user data, inventory, static data
3 physical servers
- Cassandra `" metadata, tracking messages
10 Kafka servers `" tracking message aggregation and batch insert
✑ Application servers `" customer front end, middleware for order/customs
60 virtual machines across 20 physical servers
- Tomcat `" Java services
- Nginx `" static content
- Batch servers
✑ Storage appliances
- iSCSI for virtual machine (VM) hosts
- Fibre Channel storage area network (FC SAN) `" SQL server storage
- Network-attached storage (NAS) image storage, logs, backups
✑ 10 Apache Hadoop /Spark servers
- Core Data Lake
- Data analysis workloads
✑ 20 miscellaneous servers
- Jenkins, monitoring, bastion hosts,
Business Requirements -
✑ Build a reliable and reproducible environment with scaled panty of production.
✑ Aggregate data in a centralized Data Lake for analysis
✑ Use historical data to perform predictive analytics on future shipments
✑ Accurately track every shipment worldwide using proprietary technology
✑ Improve business agility and speed of innovation through rapid provisioning of new resources
✑ Analyze and optimize architecture for performance in the cloud
✑ Migrate fully to the cloud if all other requirements are met
Technical Requirements -
Handle both streaming and batch data
Question
✑ Migrate existing Hadoop workloads
✑ Ensure architecture is scalable and elastic to meet the changing demands of the company.
✑ Use managed services whenever possible
✑ Encrypt data flight and at rest
✑ Connect a VPN between the production data center and cloud environment
SEO Statement -
We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.
We need to organize our information so we can more easily understand where our customers are and what they are shipping.
CTO Statement -
IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.
CFO Statement -
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment.
Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?- ACreate a view on the table to present to the virtualization tool.
- BCreate an additional table with only the necessary columns.
- CExport the data into a Google Sheet for virtualization.
- DCreate identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.
153You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible.
What should you do?- ALoad the data every 30 minutes into a new partitioned table in BigQuery.
- BStore the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
- CStore and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
- DStore the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
154You are designing a data processing pipeline. The pipeline must be able to scale automatically as load increases. Messages must be processed at least once and must be ordered within windows of 1 hour. How should you design the solution?
- AUse Cloud Pub/Sub for message ingestion and Cloud Dataproc for streaming analysis.
- BUse Apache Kafka for message ingestion and use Cloud Dataflow for streaming analysis.
- CUse Apache Kafka for message ingestion and use Cloud Dataproc for streaming analysis.
- DUse Cloud Pub/Sub for message ingestion and Cloud Dataflow for streaming analysis.
155Your analytics team wants to build a simple statistical model to determine which customers are most likely to work with your company again, based on a few different metrics. They want to run the model on Apache Spark, using data housed in Google Cloud Storage, and you have recommended using Google Cloud
Dataproc to execute this job. Testing has shown that this workload can run in approximately 30 minutes on a 15-node cluster, outputting the results into Google
BigQuery. The plan is to run this workload weekly. How should you optimize the cluster for cost?- AUse a higher-memory node so that the job runs faster
- BMigrate the workload to Google Cloud Dataflow
- CUse pre-emptible virtual machines (VMs) for the cluster
- DUse SSDs on the worker nodes so that the job can run faster
156The marketing team at your organization provides regular updates of a segment of your customer dataset. The marketing team has given you a CSV with 1 million records that must be updated in BigQuery. When you use the UPDATE statement in BigQuery, you receive a quotaExceeded error. What should you do?
- ASplit the source CSV file into smaller CSV files in Cloud Storage to reduce the number of BigQuery UPDATE DML statements per BigQuery job.
- BReduce the number of records updated each day to stay within the BigQuery UPDATE DML statement limit.
- CIncrease the BigQuery UPDATE DML statement limit in the Quota management section of the Google Cloud Platform Console.
- DImport the new records from the CSV file into a new BigQuery table. Create a BigQuery job that merges the new records with the existing records and writes the results to a new BigQuery table.
157You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?
- AAdd a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
- BAdd a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
- CAdd a SideInput that returns a Boolean if the element is corrupt.
- DAdd a ParDo transform in Cloud Dataflow to discard corrupt elements.
158You are developing a new deep learning model that predicts a customer's likelihood to buy on your ecommerce site. After running an evaluation of the model against both the original training data and new test data, you find that your model is overfitting the data. You want to improve the accuracy of the model when predicting new data. What should you do?
- AReduce the size of the training dataset, and increase the number of input features.
- BIncrease the size of the training dataset, and decrease the number of input features.
- CIncrease the size of the training dataset, and increase the number of input features.
- DReduce the size of the training dataset, and decrease the number of input features.
159You are preparing an organization-wide dataset. You need to preprocess customer data stored in a restricted bucket in Cloud Storage. The data will be used to create consumer analyses. You need to follow data privacy requirements, including protecting certain sensitive data elements, while also retaining all of the data for potential future use cases. What should you do?
- AUse customer-managed encryption keys (CMEK) to directly encrypt the data in Cloud Storage. Use federated queries from BigQuery. Share the encryption key by following the principle of least privilege.
- BUse Dataflow and the Cloud Data Loss Prevention API to mask sensitive data. Write the processed data in BigQuery.
- CUse the Cloud Data Loss Prevention API and Dataflow to detect and remove sensitive fields from the data in Cloud Storage. Write the filtered data in BigQuery.
- DUse Dataflow and Cloud KMS to encrypt sensitive fields and write the encrypted data in BigQuery. Share the encryption key by following the principle of least privilege.
160Your company uses a proprietary system to send inventory data every 6 hours to a data ingestion service in the cloud. Transmitted data includes a payload of several fields and the timestamp of the transmission. If there are any concerns about a transmission, the system re-transmits the data. How should you deduplicate the data most efficiency?
- AAssign global unique identifiers (GUID) to each data entry.
- BCompute the hash value of each data entry, and compare it with all historical data.
- CMaintain a database table to store the hash value and other metadata for each data entry.
- DStore each data entry as the primary key in a separate database and apply an index.
161You work for a manufacturing plant that batches application log files together into a single log file once a day at 2:00 AM. You have written a Google Cloud
Dataflow job to process that log file. You need to make sure the log file in processed once per day as inexpensively as possible. What should you do?- AChange the processing job to use Google Cloud Dataproc instead.
- BCreate a cron job with Google App Engine Cron Service to run the Cloud Dataflow job.
- CManually start the Cloud Dataflow job each morning when you get into the office.
- DConfigure the Cloud Dataflow job as a streaming job so that it processes the log data immediately.
162Your globally distributed auction application allows users to bid on items. Occasionally, users place identical bids at nearly identical times, and different application servers process those bids. Each bid event contains the item, amount, user, and timestamp. You want to collate those bid events into a single location in real time to determine which user bid first. What should you do?
- ASet up a MySQL database for each application server to write bid events into. Periodically query each of those distributed MySQL databases and update a master MySQL database with bid event information.
- BHave each application server write the bid events to Google Cloud Pub/Sub as they occur. Use a pull subscription to pull the bid events using Google Cloud Dataflow. Give the bid for each item to the user in the bid event that is processed first.
- CHave each application server write the bid events to Cloud Pub/Sub as they occur. Push the events from Cloud Pub/Sub to a custom endpoint that writes the bid event information into Cloud SQL.
- DCreate a file on a shared file and have the application servers write all bid events to that file. Process the file with Apache Hadoop to identify which user bid first.
163Your company is running their first dynamic campaign, serving different offers by analyzing real-time data during the holiday season. The data scientists are collecting terabytes of data that rapidly grows every hour during their 30-day campaign. They are using Google Cloud Dataflow to preprocess the data and collect the feature (signals) data that is needed for the machine learning model in Google Cloud Bigtable. The team is observing suboptimal performance with reads and writes of their initial load of 10 TB of data. They want to improve this performance while minimizing cost. What should they do?
- AThe performance issue should be resolved over time as the site of the BigDate cluster is increased.
- BRedefine the schema by evenly distributing reads and writes across the row space of the table.
- CRedesign the schema to use a single row key to identify values that need to be updated frequently in the cluster.
- DRedesign the schema to use row keys based on numeric IDs that increase sequentially per user viewing the offers.
164You are designing a data warehouse in BigQuery to analyze sales data for a telecommunication service provider. You need to create a data model for customers, products, and subscriptions. All customers, products, and subscriptions can be updated monthly, but you must maintain a historical record of all data. You plan to use the visualization layer for current and historical reporting. You need to ensure that the data model is simple, easy-to-use, and cost-effective. What should you do?
- ACreate a denormalized, append-only model with nested and repeated fields. Use the ingestion timestamp to track historical data.
- BCreate a normalized model with tables for each entity. Keep all input files in a Cloud Storage bucket to track historical data.
- CCreate a denormalized model with nested and repeated fields. Update the table and use snapshots to track historical data.
- DCreate a normalized model with tables for each entity. Use snapshots before updates to track historical data.
165You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of data. What should you do?
- ABuild an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud SQL, and join and filter the predicted labels to match the user's viewing history to generate preferences.
- BBuild and train a complex classification model with Spark MLlib to generate labels and filter the results. Deploy the models using Cloud Dataproc. Call the model from your application.
- CBuild and train a classification model with Spark MLlib to generate labels. Build and train a second classification model with Spark MLlib to filter results to match customer preferences. Deploy the models using Cloud Dataproc. Call the models from your application.
- DBuild an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.
166A TensorFlow machine learning model on Compute Engine virtual machines (n2-standard-32) takes two days to complete training. The model has custom TensorFlow operations that must run partially on a CPU. You want to reduce the training time in a cost-effective manner. What should you do?
- AChange the VM type to n2-highmem-32.
- BTrain the model using a VM with a TPU hardware accelerator.
- CChange the VM type to e2-standard-32.
- DTrain the model using a VM with a GPU hardware accelerator.
167Your organization is modernizing their IT services and migrating to Google Cloud. You need to organize the data that will be stored in Cloud Storage and BigQuery. You need to enable a data mesh approach to share the data between sales, product design, and marketing departments. What should you do?
- A1. Create a project for storage of the data for each of your departments.
2. Enable each department to create Cloud Storage buckets and BigQuery datasets.
3. Create user groups for authorized readers for each bucket and dataset.
4. Enable the IT team to administer the user groups to add or remove users as the departments’ request. - B1. Create multiple projects for storage of the data for each of your departments’ applications.
2. Enable each department to create Cloud Storage buckets and BigQuery datasets.
3. Publish the data that each department shared in Analytics Hub.
4. Enable all departments to discover and subscribe to the data they need in Analytics Hub. - C1. Create a project for storage of the data for your organization.
2. Create a central Cloud Storage bucket with three folders to store the files for each department.
3. Create a central BigQuery dataset with tables prefixed with the department name.
4. Give viewer rights for the storage project for the users of your departments. - D1. Create multiple projects for storage of the data for each of your departments’ applications.
2. Enable each department to create Cloud Storage buckets and BigQuery datasets.
3. In Dataplex, map each department to a data lake and the Cloud Storage buckets, and map the BigQuery datasets to zones.
4. Enable each department to own and share the data of their data lakes.
- A1. Create a project for storage of the data for each of your departments.
168You are using BigQuery with a multi-region dataset that includes a table with the daily sales volumes. This table is updated multiple times per day. You need to protect your sales table in case of regional failures with a recovery point objective (RPO) of less than 24 hours, while keeping costs to a minimum. What should you do?
- ASchedule a daily export of the table to a Cloud Storage dual or multi-region bucket.
- BModify ETL job to load the data into both the current and another backup region.
- CSchedule a daily copy of the dataset to a backup region.
- DSchedule a daily BigQuery snapshot of the table.
169You stream order data by using a Dataflow pipeline, and write the aggregated result to Memorystore. You provisioned a Memorystore for Redis instance with Basic Tier, 4 GB capacity, which is used by 40 clients for read-only access. You are expecting the number of read-only clients to increase significantly to a few hundred and you need to be able to support the demand. You want to ensure that read and write access availability is not impacted, and any changes you make can be deployed quickly. What should you do?
- ACreate multiple new Memorystore for Redis instances with Basic Tier (4 GB capacity). Modify the Dataflow pipeline and new clients to use all instances.
- BCreate a new Memorystore for Redis instance with Standard Tier. Set capacity to 4 GB and read replica to No read replicas (high availability only). Delete the old instance.
- CCreate a new Memorystore for Memcached instance. Set a minimum of three nodes, and memory per node to 4 GB. Modify the Dataflow pipeline and all clients to use the Memcached instance. Delete the old instance.
- DCreate a new Memorystore for Redis instance with Standard Tier. Set capacity to 5 GB and create multiple read replicas. Delete the old instance.
170You designed a data warehouse in BigQuery to analyze sales data. You want a self-serving, low-maintenance, and cost- effective solution to share the sales dataset to other business units in your organization. What should you do?
- AEnable the other business units’ projects to access the authorized views of the sales dataset.
- BCreate and share views with the users in the other business units.
- CCreate an Analytics Hub private exchange, and publish the sales dataset.
- DUse the BigQuery Data Transfer Service to create a schedule that copies the sales dataset to the other business units’ projects.
171You are running your BigQuery project in the on-demand billing model and are executing a change data capture (CDC) process that ingests data. The CDC process loads 1 GB of data every 10 minutes into a temporary table, and then performs a merge into a 10 TB target table. This process is very scan intensive and you want to explore options to enable a predictable cost model. You need to create a BigQuery reservation based on utilization information gathered from BigQuery Monitoring and apply the reservation to the CDC process. What should you do?
- ACreate a BigQuery reservation for the job.
- BCreate a BigQuery reservation for the project.
- CCreate a BigQuery reservation for the dataset.
- DCreate a BigQuery reservation for the service account running the job.
172Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?
- ADimensionality Reduction
- BDropout Methods
- CThreading
- DSerialization
173You maintain ETL pipelines. You notice that a streaming pipeline running on Dataflow is taking a long time to process incoming data, which causes output delays. You also noticed that the pipeline graph was automatically optimized by Dataflow and merged into one step. You want to identify where the potential bottleneck is occurring. What should you do?
- AInsert output sinks after each key processing step, and observe the writing throughput of each block.
- BLog debug information in each ParDo function, and analyze the logs at execution time.
- CVerify that the Dataflow service accounts have appropriate permissions to write the processed data to the output sinks.
- DInsert a Reshuffle operation after each processing step, and monitor the execution details in the Dataflow console.
174Your neural network model is taking days to train. You want to increase the training speed. What can you do?
- AIncrease the number of layers in your neural network.
- BIncrease the number of input features to your model.
- CSubsample your training dataset.
- DSubsample your test dataset.
175You have a BigQuery dataset named “customers”. All tables will be tagged by using a Data Catalog tag template named “gdpr”. The template contains one mandatory field, “has_sensitive_data”, with a boolean value. All employees must be able to do a simple search and find tables in the dataset that have either true or false in the “has_sensitive_data’ field. However, only the Human Resources (HR) group should be able to see the data inside the tables for which “has_sensitive data” is true. You give the all employees group the bigquery.metadataViewer and bigquery.connectionUser roles on the dataset. You want to minimize configuration overhead. What should you do next?
- ACreate the “gdpr” tag template with public visibility. Assign the datacatalog.tagTemplateViewer role on this tag to the all employees group, and assign the bigquery.dataViewer role to the HR group on the tables that contain sensitive data.
- BCreate the “gdpr” tag template with public visibility. Assign the bigquery.dataViewer role to the HR group on the tables that contain sensitive data.
- CCreate the “gdpr” tag template with private visibility. Assign the datacatalog.tagTemplateViewer role on this tag to the all employees group, and assign the bigquery.dataViewer role to the HR group on the tables that contain sensitive data.
- DCreate the “gdpr” tag template with private visibility. Assign the bigquery.dataViewer role to the HR group on the tables that contain sensitive data.
176You have a BigQuery table that contains customer data, including sensitive information such as names and addresses. You need to share the customer data with your data analytics and consumer support teams securely. The data analytics team needs to access the data of all the customers, but must not be able to access the sensitive data. The consumer support team needs access to all data columns, but must not be able to access customers that no longer have active contracts. You enforced these requirements by using an authorized dataset and policy tags. After implementing these steps, the data analytics team reports that they still have access to the sensitive columns. You need to ensure that the data analytics team does not have access to restricted data. What should you do? (Choose two.)
- ARemove the bigquery.dataViewer role from the data analytics team on the authorized datasets.
- BEnforce access control in the policy tag taxonomy.
- CCreate two separate authorized datasets; one for the data analytics team and another for the consumer support team.
- DEnsure that the data analytics team members do not have the Data Catalog Fine-Grained Reader role for the policy tags.
- EReplace the authorized dataset with an authorized view. Use row-level security and apply filter_expression to limit data access.
177You need to look at BigQuery data from a specific table multiple times a day. The underlying table you are querying is several petabytes in size, but you want to filter your data and provide simple aggregations to downstream users. You want to run queries faster and get up-to-date insights quicker. What should you do?
- ARun a scheduled query to pull the necessary data at specific intervals dally.
- BUse a cached query to accelerate time to results.
- CLimit the query columns being pulled in the final result.
- DCreate a materialized view based off of the query being run.
178You created an analytics environment on Google Cloud so that your data scientist team can explore data without impacting the on-premises Apache Hadoop solution. The data in the on-premises Hadoop Distributed File System (HDFS) cluster is in Optimized Row Columnar (ORC) formatted files with multiple columns of Hive partitioning. The data scientist team needs to be able to explore the data in a similar way as they used the on-premises HDFS cluster with SQL on the Hive query engine. You need to choose the most cost-effective storage and processing solution. What should you do?
- ACopy the ORC files on Cloud Storage, then create external BigQuery tables for the data scientist team.
- BImport the ORC files to BigQuery tables for the data scientist team.
- CImport the ORC files to Bigtable tables for the data scientist team.
- DCopy the ORC files on Cloud Storage, then deploy a Dataproc cluster for the data scientist team.
179Your infrastructure team has set up an interconnect link between Google Cloud and the on-premises network. You are designing a high-throughput streaming pipeline to ingest data in streaming from an Apache Kafka cluster hosted on- premises. You want to store the data in BigQuery, with as minimal latency as possible. What should you do?
- AUse Dataflow, write a pipeline that reads the data from Kafka, and writes the data to BigQuery.
- BSetup a Kafka Connect bridge between Kafka and Pub/Sub. Use a Google-provided Dataflow template to read the data from Pub/Sub, and write the data to BigQuery.
- CUse a proxy host in the VPC in Google Cloud connecting to Kafka. Write a Dataflow pipeline, read data from the proxy host, and write the data to BigQuery.
- DSetup a Kafka Connect bridge between Kafka and Pub/Sub. Write a Dataflow pipeline, read the data from Pub/Sub, and write the data to BigQuery.
180You are developing a model to identify the factors that lead to sales conversions for your customers. You have completed processing your data. You want to continue through the model development lifecycle. What should you do next?
- ATest and evaluate your model on your curated data to determine how well the model performs.
- BUse your model to run predictions on fresh customer input data.
- CMonitor your model performance, and make any adjustments needed.
- DDelineate what data will be used for testing and what will be used for training the model.
181You're using Bigtable for a real-time application, and you have a heavy load that is a mix of read and writes. You've recently identified an additional use case and need to perform hourly an analytical job to calculate certain statistics across the whole database. You need to ensure both the reliability of your production application as well as the analytical workload.
- AIncrease the size of your existing cluster twice and execute your analytics workload on your new resized cluster.
- BAdd a second cluster to an existing instance with a multi-cluster routing, use live-traffic app profile for your regular workload and batch-analytics profile for the analytics workload.
- CAdd a second cluster to an existing instance with a single-cluster routing, use live-traffic app profile for your regular workload and batch-analytics profile for the analytics workload.
- DExport Bigtable dump to GCS and run your analytical job on top of the exported files.
182One of your encryption keys stored in Cloud Key Management Service (Cloud KMS) was exposed. You need to re- encrypt all of your CMEK-protected Cloud Storage data that used that key, and then delete the compromised key. You also want to reduce the risk of objects getting written without customer-managed encryption key (CMEK) protection in the future. What should you do?
- ACreate a new Cloud KMS key. Create a new Cloud Storage bucket configured to use the new key as the default CMEK key. Copy all objects from the old bucket to the new bucket without specifying a key.
- BCreate a new Cloud KMS key. Set the default CMEK key on the existing Cloud Storage bucket to the new one.
- CCreate a new Cloud KMS key. Create a new Cloud Storage bucket. Copy all objects from the old bucket to the new one bucket while specifying the new Cloud KMS key in the copy command.
- DRotate the Cloud KMS key version. Continue to use the same Cloud Storage bucket.
183MJTelco Case Study -
Company Overview -
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background -
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept -
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
✑ Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
✑ Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments `" development/test, staging, and production `" to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements -
✑ Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.
✑ Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
✑ Provide reliable and timely access to data for analysis from distributed research workers
✑ Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements -
Ensure secure and efficient transport and storage of telemetry data
Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately 100m records/day
Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement -
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement -
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement -
The project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
Given the record streams MJTelco is interested in ingesting per day, they are concerned about the cost of Google BigQuery increasing. MJTelco asks you to provide a design solution. They require a single large data table called tracking_table. Additionally, they want to minimize the cost of daily queries while performing fine-grained analysis of each day's events. They also want to use streaming ingestion. What should you do?- ACreate a table called tracking_table with a TIMESTAMP column to represent the day.
- BCreate sharded tables for each day following the pattern tracking_table_YYYYMMDD.
- CCreate a partitioned table called tracking_table and include a TIMESTAMP column.
- DCreate a table called tracking_table and include a DATE column.
184You are analyzing the price of a company's stock. Every 5 seconds, you need to compute a moving average of the past 30 seconds' worth of data. You are reading data from Pub/Sub and using DataFlow to conduct the analysis. How should you set up your windowed pipeline?
- AUse a sliding window with a duration of 5 seconds. Emit results by setting the following trigger: AfterProcessingTime.pastFirstElementInPane().plusDelayOf (Duration.standardSeconds(30))
- BUse a sliding window with a duration of 30 seconds and a period of 5 seconds. Emit results by setting the following trigger: AfterWatermark.pastEndOfWindow ()
- CUse a fixed window with a duration of 5 seconds. Emit results by setting the following trigger: AfterProcessingTime.pastFirstElementInPane().plusDelayOf (Duration.standardSeconds(30))
- DUse a fixed window with a duration of 30 seconds. Emit results by setting the following trigger: AfterWatermark.pastEndOfWindow().plusDelayOf (Duration.standardSeconds(5))
185You have two projects where you run BigQuery jobs:
• One project runs production jobs that have strict completion time SLAs. These are high priority jobs that must have the required compute resources available when needed. These jobs generally never go below a 300 slot utilization, but occasionally spike up an additional 500 slots.
• The other project is for users to run ad-hoc analytical queries. This project generally never uses more than 200 slots at a time. You want these ad-hoc queries to be billed based on how much data users scan rather than by slot capacity.
You need to ensure that both projects have the appropriate compute resources available. What should you do?- ACreate a single Enterprise Edition reservation for both projects. Set a baseline of 300 slots. Enable autoscaling up to 700 slots.
- BCreate two reservations, one for each of the projects. For the SLA project, use an Enterprise Edition with a baseline of 300 slots and enable autoscaling up to 500 slots. For the ad-hoc project, configure on-demand billing.
- CCreate two Enterprise Edition reservations, one for each of the projects. For the SLA project, set a baseline of 800 slots. For the ad-hoc project, enable autoscaling up to 200 slots.
- DCreate two Enterprise Edition reservations, one for each of the projects. For the SLA project, set a baseline of 300 slots and enable autoscaling up to 500 slots. For the ad-hoc project, set a reservation baseline of 0 slots and set the ignore idle slots flag to False.
186You have important legal hold documents in a Cloud Storage bucket. You need to ensure that these documents are not deleted or modified. What should you do?
- AEnable the Object Versioning feature. Create a copy in a bucket in a different region.
- BEnable the Object Versioning feature. Add a lifecycle rule.
- CSet a retention policy. Lock the retention policy.
- DSet a retention policy. Set the default storage class to Archive for long-term digital preservation.
187MJTelco Case Study -
Company Overview -
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background -
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept -
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
✑ Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
Question
MJTelco will also use three separate operating environments `" development/test, staging, and production `" to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements -
✑ Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.
✑ Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
✑ Provide reliable and timely access to data for analysis from distributed research workers
✑ Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements -
Ensure secure and efficient transport and storage of telemetry data
Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately 100m records/day
Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement -
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement -
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement -
The project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
You need to compose visualization for operations teams with the following requirements:
✑ Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling once every minute)
✑ The report must not be more than 3 hours delayed from live data.
✑ The actionable report should only show suboptimal links.
✑ Most suboptimal links should be sorted to the top.
Suboptimal links can be grouped and filtered by regional geography.
Question
✑ User response time to load the report must be <5 seconds.
You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types. You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month. What should you do?- ALoad the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.
- BLook through the current data and compose a series of charts and tables, one for each possible combination of criteria.
- CLook through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.
- DExport the data to a spreadsheet, compose a series of charts and tables, one for each possible combination of criteria, and spread them across multiple tabs.
188You are monitoring your organization’s data lake hosted on BigQuery. The ingestion pipelines read data from Pub/Sub and write the data into tables on BigQuery. After a new version of the ingestion pipelines is deployed, the daily stored data increased by 50%. The volumes of data in Pub/Sub remained the same and only some tables had their daily partition data size doubled. You need to investigate and fix the cause of the data increase. What should you do?
- A1. Check for duplicate rows in the BigQuery tables that have the daily partition data size doubled.
2. Schedule daily SQL jobs to deduplicate the affected tables.
3. Share the deduplication script with the other operational teams to reuse if this occurs to other tables. - B1. Check for duplicate rows in the BigQuery tables that have the daily partition data size doubled.
2. Check the BigQuery Audit logs to find job IDs.
3. Use Cloud Monitoring to determine when the identified Dataflow jobs started and the pipeline code version.
4. When more than one pipeline ingests data into a table, stop all versions except the latest one. - C1. Check for code errors in the deployed pipelines.
2. Check for multiple writing to pipeline BigQuery sink.
3. Check for errors in Cloud Logging during the day of the release of the new pipelines.
4. If no errors, restore the BigQuery tables to their content before the last release by using time travel. - D1. Roll back the last deployment.
2. Restore the BigQuery tables to their content before the last release by using time travel.
3. Restart the Dataflow jobs and replay the messages by seeking the subscription to the timestamp of the release.
- A1. Check for duplicate rows in the BigQuery tables that have the daily partition data size doubled.
189As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects.
Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? (Choose two.)- AOnly use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
- BFor each Cloud Storage bucket or BigQuery dataset, decide which projects need access. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
- CUse Cloud Deployment Manager to automate access provision.
- DCreate distinct groups for various teams, and specify groups in Cloud IAM policies.
- EIntroduce resource hierarchy to leverage access control policy inheritance.
190You operate a database that stores stock trades and an application that retrieves average stock price for a given company over an adjustable window of time. The data is stored in Cloud Bigtable where the datetime of the stock trade is the beginning of the row key. Your application has thousands of concurrent users, and you notice that performance is starting to degrade as more stocks are added. What should you do to improve the performance of your application?
- AChange the row key syntax in your Cloud Bigtable table to begin with a random number per second.
- BChange the data pipeline to use BigQuery for storing stock trades, and update your application.
- CChange the row key syntax in your Cloud Bigtable table to begin with the stock symbol.
- DUse Cloud Dataflow to write a summary of each day's stock trades to an Avro file on Cloud Storage. Update your application to read from Cloud Storage and Cloud Bigtable to compute the responses.
191You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?
- AUse Kafka Connect to link your Kafka message queue to Pub/Sub. Use a Dataflow template to write your messages from Pub/Sub to Bigtable. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Bigtable in the last hour. If that number falls below 4000, send an alert.
- BConsume the stream of data in Dataflow using Kafka IO. Set a sliding time window of 1 hour every 5 minutes. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
- CUse Kafka Connect to link your Kafka message queue to Pub/Sub. Use a Dataflow template to write your messages from Pub/Sub to BigQuery. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour. If that number falls below 4000, send an alert.
- DConsume the stream of data in Dataflow using Kafka IO. Set a fixed time window of 1 hour. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
192You are using Bigtable to persist and serve stock market data for each of the major indices. To serve the trading application, you need to access only the most recent stock prices that are streaming in. How should you design your row key and tables to ensure that you can access the data with the simplest query?
- AFor each index, have a separate table and use a reverse timestamp as the row key design.
- BCreate one unique table for all of the indices, and then use a reverse timestamp as the row key design.
- CFor each index, have a separate table and use a timestamp as the row key design.
- DCreate one unique table for all of the indices, and then use the index and timestamp as the row key design.
193Your team is working on a binary classification problem. You have trained a support vector machine (SVM) classifier with default parameters, and received an area under the Curve (AUC) of 0.87 on the validation set. You want to increase the AUC of the model. What should you do?
- ATrain a classifier with deep neural networks, because neural networks would always beat SVMs
- BDeploy the model and measure the real-world AUC; it's always higher because of generalization
- CScale predictions you get out of the model (tune a scaling factor as a hyperparameter) in order to get the highest AUC
- DPerform hyperparameter tuning
194You use a dataset in BigQuery for analysis. You want to provide third-party companies with access to the same dataset. You need to keep the costs of data sharing low and ensure that the data is current. Which solution should you choose?
- ACreate a Dataflow job that reads the data in frequent time intervals, and writes it to the relevant BigQuery dataset or Cloud Storage bucket for third-party companies to use.
- BUse Cloud Scheduler to export the data on a regular basis to Cloud Storage, and provide third-party companies with access to the bucket.
- CUse Analytics Hub to control data access, and provide third party companies with access to the dataset.
- DCreate a separate dataset in BigQuery that contains the relevant data to share, and provide third-party companies with access to the new dataset.
195You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single resource-constrained virtual machine. Which learning algorithm should you use?
- AFeedforward neural network
- BRecurrent neural network
- CLinear regression
- DLogistic classification
196You have several Spark jobs that run on a Cloud Dataproc cluster on a schedule. Some of the jobs run in sequence, and some of the jobs run concurrently. You need to automate this process. What should you do?
- ACreate a Directed Acyclic Graph in Cloud Composer
- BCreate a Bash script that uses the Cloud SDK to create a cluster, execute jobs, and then tear down the cluster
- CCreate an initialization action to execute the jobs
- DCreate a Cloud Dataproc Workflow Template
197You have a petabyte of analytics data and need to design a storage and processing platform for it. You must be able to perform data warehouse-style analytics on the data in Google Cloud and expose the dataset as files for batch analysis tools in other cloud providers. What should you do?
- AStore and process the entire dataset in Bigtable.
- BStore and process the entire dataset in BigQuery.
- CStore the full dataset in BigQuery, and store a compressed copy of the data in a Cloud Storage bucket.
- DStore the warm data as files in Cloud Storage, and store the active data in BigQuery. Keep this ratio as 80% warm and 20% active.
198You have a Cloud SQL for PostgreSQL instance in Region’ with one read replica in Region2 and another read replica in Region3. An unexpected event in Region’ requires that you perform disaster recovery by promoting a read replica in Region2. You need to ensure that your application has the same database capacity available before you switch over the connections. What should you do?
- AEnable zonal high availability on the primary instance. Create a new read replica in a new region.
- BCreate a new read replica in Region1, promote the new read replica to be the primary instance, and enable zonal high availability.
- CCreate a cascading read replica from the existing read replica in Region3.
- DCreate two new read replicas from the new primary instance, one in Region3 and one in a new region.
199You have an upstream process that writes data to Cloud Storage. This data is then read by an Apache Spark job that runs on Dataproc. These jobs are run in the us-central1 region, but the data could be stored anywhere in the United States. You need to have a recovery process in place in case of a catastrophic single region failure. You need an approach with a maximum of 15 minutes of data loss (RPO=15 mins). You want to ensure that there is minimal latency when reading the data. What should you do?
- A1. Create a dual-region Cloud Storage bucket in the us-central1 and us-south1 regions.
2. Enable turbo replication.
3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in the same region.
4. In case of a regional failure, redeploy the Dataproc clusters to the us-south1 region and read from the same bucket. - B1. Create a dual-region Cloud Storage bucket in the us-central1 and us-south1 regions.
2. Enable turbo replication.
3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in the us-south1 region.
4. In case of a regional failure, redeploy your Dataproc cluster to the us-south1 region and continue reading from the same bucket. - C1. Create two regional Cloud Storage buckets, one in the us-central1 region and one in the us-south1 region.
2. Have the upstream process write data to the us-central1 bucket. Use the Storage Transfer Service to copy data hourly from the us-central1 bucket to the us-south1 bucket.
3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in that region.
4. In case of regional failure, redeploy your Dataproc clusters to the us-south1 region and read from the bucket in that region instead. - D1. Create a Cloud Storage bucket in the US multi-region.
2. Run the Dataproc cluster in a zone in the us-central1 region, reading data from the US multi-region bucket.
3. In case of a regional failure, redeploy the Dataproc cluster to the us-central2 region and continue reading from the same bucket.
- A1. Create a dual-region Cloud Storage bucket in the us-central1 and us-south1 regions.
200You are using BigQuery with a regional dataset that includes a table with the daily sales volumes. This table is updated multiple times per day. You need to protect your sales table in case of regional failures with a recovery point objective (RPO) of less than 24 hours, while keeping costs to a minimum. What should you do?
- ASchedule a daily copy of the dataset to a backup region.
- BSchedule a daily BigQuery snapshot of the table.
- CModify ETL job to load the data into both the current and another backup region.
- DSchedule a daily export of the table to a Cloud Storage dual or multi-region bucket.
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- Quiz on GCP - Professional Data Engineer1_2, created by raul paredes on 04/02/2025.
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- raul paredes
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