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Google Cloud Professional Cloud Architect PT 13
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Questions in this quiz (21)
1TerramEarth's CTO wants to use the raw data from connected vehicles to help identify approximately when a vehicle in the field will have a catastrophic failure.
You want to allow analysts to centrally query the vehicle data.
Which architecture should you recommend?- AA.
- BB.
- CC.
- DD.
- AA.
2The TerramEarth development team wants to create an API to meet the company's business requirements. You want the development team to focus their development effort on business value versus creating a custom framework.
Which method should they use?- AA. Use Google App Engine with Google Cloud Endpoints. Focus on an API for dealers and partners
- BB. Use Google App Engine with a JAX-RS Jersey Java-based framework. Focus on an API for the public
- CC. Use Google App Engine with the Swagger (Open API Specification) framework. Focus on an API for the public
- DD. Use Google Container Engine with a Django Python container. Focus on an API for the public
- EE. Use Google Container Engine with a Tomcat container with the Swagger (Open API Specification) framework. Focus on an API for dealers and partners
3Your development team has created a structured API to retrieve vehicle data. They want to allow third parties to develop tools for dealerships that use this vehicle event data. You want to support delegated authorization against this data.
What should you do?- AA. Build or leverage an OAuth-compatible access control system
- BB. Build SAML 2.0 SSO compatibility into your authentication system
- CC. Restrict data access based on the source IP address of the partner systems
- DD. Create secondary credentials for each dealer that can be given to the trusted third party
4TerramEarth plans to connect all 20 million vehicles in the field to the cloud. This increases the volume to 20 million 600 byte records a second for 40 TB an hour.
How should you design the data ingestion?- AA. Vehicles write data directly to GCS
- BB. Vehicles write data directly to Google Cloud Pub/Sub
- CC. Vehicles stream data directly to Google BigQuery
- DD. Vehicles continue to write data using the existing system (FTP)
5You analyzed TerramEarth's business requirement to reduce downtime, and found that they can achieve a majority of time saving by reducing customer's wait time for parts. You decided to focus on reduction of the 3 weeks aggregate reporting time.
Which modifications to the company's processes should you recommend?- AA. Migrate from CSV to binary format, migrate from FTP to SFTP transport, and develop machine learning analysis of metrics
- BB. Migrate from FTP to streaming transport, migrate from CSV to binary format, and develop machine learning analysis of metrics
- CC. Increase fleet cellular connectivity to 80%, migrate from FTP to streaming transport, and develop machine learning analysis of metrics
- DD. Migrate from FTP to SFTP transport, develop machine learning analysis of metrics, and increase dealer local inventory by a fixed factor
6Which of TerramEarth's legacy enterprise processes will experience significant change as a result of increased Google Cloud Platform adoption?
- AA. Opex/capex allocation, LAN changes, capacity planning
- BB. Capacity planning, TCO calculations, opex/capex allocation
- CC. Capacity planning, utilization measurement, data center expansion
- DD. Data Center expansion, TCO calculations, utilization measurement
7To speed up data retrieval, more vehicles will be upgraded to cellular connections and be able to transmit data to the ETL process. The current FTP process is error-prone and restarts the data transfer from the start of the file when connections fail, which happens often. You want to improve the reliability of the solution and minimize data transfer time on the cellular connections.
What should you do?- AA. Use one Google Container Engine cluster of FTP servers. Save the data to a Multi-Regional bucket. Run the ETL process using data in the bucket
- BB. Use multiple Google Container Engine clusters running FTP servers located in different regions. Save the data to Multi-Regional buckets in US, EU, and Asia. Run the ETL process using the data in the bucket
- CC. Directly transfer the files to different Google Cloud Multi-Regional Storage bucket locations in US, EU, and Asia using Google APIs over HTTP(S). Run the ETL process using the data in the bucket
- DD. Directly transfer the files to a different Google Cloud Regional Storage bucket location in US, EU, and Asia using Google APIs over HTTP(S). Run the ETL process to retrieve the data from each Regional bucket
8TerramEarth's 20 million vehicles are scattered around the world. Based on the vehicle's location, its telemetry data is stored in a Google Cloud Storage (GCS) regional bucket (US, Europe, or Asia). The CTO has asked you to run a report on the raw telemetry data to determine why vehicles are breaking down after 100 K miles. You want to run this job on all the data.
What is the most cost-effective way to run this job?- AA. Move all the data into 1 zone, then launch a Cloud Dataproc cluster to run the job
- BB. Move all the data into 1 region, then launch a Google Cloud Dataproc cluster to run the job
- CC. Launch a cluster in each region to preprocess and compress the raw data, then move the data into a multiregion bucket and use a Dataproc cluster to finish the job
- DD. Launch a cluster in each region to preprocess and compress the raw data, then move the data into a region bucket and use a Cloud Dataproc cluster to finish the job
9TerramEarth has equipped all connected trucks with servers and sensors to collect telemetry data. Next year they want to use the data to train machine learning models. They want to store this data in the cloud while reducing costs.
What should they do?- AA. Have the vehicle's computer compress the data in hourly snapshots, and store it in a Google Cloud Storage (GCS) Nearline bucket
- BB. Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Google BigQuery
- CC. Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Cloud Bigtable
- DD. Have the vehicle's computer compress the data in hourly snapshots, and store it in a GCS Coldline bucket
10Your agricultural division is experimenting with fully autonomous vehicles. You want your architecture to promote strong security during vehicle operation.
Which two architectures should you consider? (Choose two.)- AA. Treat every micro service call between modules on the vehicle as untrusted.
- BB. Require IPv6 for connectivity to ensure a secure address space.
- CC. Use a trusted platform module (TPM) and verify firmware and binaries on boot.
- DD. Use a functional programming language to isolate code execution cycles.
- EE. Use multiple connectivity subsystems for redundancy.
- FF. Enclose the vehicle's drive electronics in a Faraday cage to isolate chips.
11Operational parameters such as oil pressure are adjustable on each of TerramEarth's vehicles to increase their efficiency, depending on their environmental conditions. Your primary goal is to increase the operating efficiency of all 20 million cellular and unconnected vehicles in the field.
How can you accomplish this goal?- AA. Have you engineers inspect the data for patterns, and then create an algorithm with rules that make operational adjustments automatically
- BB. Capture all operating data, train machine learning models that identify ideal operations, and run locally to make operational adjustments automatically
- CC. Implement a Google Cloud Dataflow streaming job with a sliding window, and use Google Cloud Messaging (GCM) to make operational adjustments automatically
- DD. Capture all operating data, train machine learning models that identify ideal operations, and host in Google Cloud Machine Learning (ML) Platform to make operational adjustments automatically
12For this question, refer to the TerramEarth case study. To be compliant with European GDPR regulation, TerramEarth is required to delete data generated from its European customers after a period of 36 months when it contains personal data. In the new architecture, this data will be stored in both Cloud Storage and
BigQuery. What should you do?- AA. Create a BigQuery table for the European data, and set the table retention period to 36 months. For Cloud Storage, use gsutil to enable lifecycle management using a DELETE action with an Age condition of 36 months.
- BB. Create a BigQuery table for the European data, and set the table retention period to 36 months. For Cloud Storage, use gsutil to create a SetStorageClass to NONE action when with an Age condition of 36 months.
- CC. Create a BigQuery time-partitioned table for the European data, and set the partition expiration period to 36 months. For Cloud Storage, use gsutil to enable lifecycle management using a DELETE action with an Age condition of 36 months.
- DD. Create a BigQuery time-partitioned table for the European data, and set the partition expiration period to 36 months. For Cloud Storage, use gsutil to create a SetStorageClass to NONE action with an Age condition of 36 months.
13For this question, refer to the TerramEarth case study. TerramEarth has decided to store data files in Cloud Storage. You need to configure Cloud Storage lifecycle rule to store 1 year of data and minimize file storage cost. Which two actions should you take?
- AA. Create a Cloud Storage lifecycle rule with Age: 30, Storage Class: Standard, and Action: Set to Coldline, and create a second GCS life-cycle rule with Age: 365, Storage Class: Coldline, and Action: Delete.
- BB. Create a Cloud Storage lifecycle rule with Age: 30, Storage Class: Coldline, and Action: Set to Nearline, and create a second GCS life-cycle rule with Age: 91, Storage Class: Coldline, and Action: Set to Nearline.
- CC. Create a Cloud Storage lifecycle rule with Age: 90, Storage Class: Standard, and Action: Set to Nearline, and create a second GCS life-cycle rule with Age: 91, Storage Class: Nearline, and Action: Set to Coldline.
- DD. Create a Cloud Storage lifecycle rule with Age: 30, Storage Class: Standard, and Action: Set to Coldline, and create a second GCS life-cycle rule with Age: 365, Storage Class: Nearline, and Action: Delete.
14For this question, refer to the TerramEarth case study. You need to implement a reliable, scalable GCP solution for the data warehouse for your company, TerramEarth.
Considering the TerramEarth business and technical requirements, what should you do?- AA. Replace the existing data warehouse with BigQuery. Use table partitioning.
- BB. Replace the existing data warehouse with a Compute Engine instance with 96 CPUs.
- CC. Replace the existing data warehouse with BigQuery. Use federated data sources.
- DD. Replace the existing data warehouse with a Compute Engine instance with 96 CPUs. Add an additional Compute Engine preemptible instance with 32 CPUs.
15For this question, refer to the TerramEarth case study. A new architecture that writes all incoming data to BigQuery has been introduced. You notice that the data is dirty, and want to ensure data quality on an automated daily basis while managing cost.
What should you do?- AA. Set up a streaming Cloud Dataflow job, receiving data by the ingestion process. Clean the data in a Cloud Dataflow pipeline.
- BB. Create a Cloud Function that reads data from BigQuery and cleans it. Trigger the Cloud Function from a Compute Engine instance.
- CC. Create a SQL statement on the data in BigQuery, and save it as a view. Run the view daily, and save the result to a new table.
- DD. Use Cloud Dataprep and configure the BigQuery tables as the source. Schedule a daily job to clean the data.
16For this question, refer to the TerramEarth case study. Considering the technical requirements, how should you reduce the unplanned vehicle downtime in GCP?
- AA. Use BigQuery as the data warehouse. Connect all vehicles to the network and stream data into BigQuery using Cloud Pub/Sub and Cloud Dataflow. Use Google Data Studio for analysis and reporting.
- BB. Use BigQuery as the data warehouse. Connect all vehicles to the network and upload gzip files to a MultiRegional Cloud Storage bucket using gcloud. Use Google Data Studio for analysis and reporting.
- CC. Use Cloud Dataproc Hive as the data warehouse. Upload gzip files to a Multi-Regional Cloud Storage bucket. Upload this data into BigQuery using gcloud. Use Google Data Studio for analysis and reporting.
- DD. Use Cloud Dataproc Hive as the data warehouse. Directly stream data into partitioned Hive tables. Use Pig scripts to analyze data.
17For this question, refer to the TerramEarth case study. You are asked to design a new architecture for the ingestion of the data of the 200,000 vehicles that are connected to a cellular network. You want to follow Googlerecommended practices. Considering the technical requirements, which components should you use for the ingestion of the data?
- AA. Google Kubernetes Engine with an SSL Ingress
- BB. Cloud IoT Core with public/private key pairs
- CC. Compute Engine with project-wide SSH keys
- DD. Compute Engine with specific SSH keys
18Regarding Cloud Storage, which option allows any user to access to a Cloud Storage resource for a limited time, using a specific URL?
- AA. Open Buckets
- BB. Temporary Resources
- CC. Signed URLs
- DD. Temporary URLs
19Of the options given, which is a NoSQL database?
- AA. Cloud Datastore
- BB. Cloud SQL
- CC. All of the given options
- DD. Cloud Storage
20Container Engine allows orchastration of what type of containers?
- AA. Blue Whale
- BB. LXC
- CC. BSD Jails
- DD. Docker
21Regarding Cloud IAM, what type of role(s) are available?
- AA. Basic roles and Compiled roles
- BB. Primitive roles and Predefined roles
- CC. Simple roles
- DD. Basic roles and Curated roles
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- Curso Interno de Empresa Google Cloud Professional Cloud Architect Quiz on Google Cloud Professional Cloud Architect PT 13, created by Emmanuel Jimenez on 25/04/2024.
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- Emmanuel Jimenez
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