Test
Test fundamentos Big Data
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Preguntas del test (110)
1Big Data dedicated to
- AEnterprise Apps
- Banalysis
2Requeriments of big data
- AProcesing unstructured data
- BProcesing only unstructured data
3Bit data addresses
- Acombine multiple unreleated datasets
- BAnother DW to enrich data
4BD type of data
- Amachine generate
- BOnly human generate
- Chidden data
5Benefits Big Data
- AEnterprise app
- Bfault and fraud detection
- Cslow procesing
6characteristics of data in big data
- Acomplex, variety, volumen, veracity, value
- Bvariety, volumen, velocity, veracity, value
- Cincompatibilities, value,velocity,volumen, veracity, many data
7characteristics only of data in BigData
- Avalue, variety, veracity
- Bvelocity, volumen, value
- Cvelocity, variety, volumen
8veracity
- Astructured data, unstructured data
- Bsignal, noise
- Csocial network
9Human-generated data examples
- Amicro bloggin
- Bweb log
- Csensor data
10value
- Amore value more veracity, more value more time
- Bmore value less veracity, more value less time
- Cmore value more veracity, more value less time
11benefits
- Aoperational optimization, noise information
- Bscientific discoveries, actionable intelligence
- Caccurate predictions, shipped cloud computing
12Dataset
- Acollections or groups of related data, shares the different set of attributes
- Bcollections or groups of related data, shares the same set of attributes
- Cdiscipline of gaining an understanding of data
13Data Analysis
- Adiscipline of gaining an understanding of data
- Bprocess of examining data to find facts, relationships, patterns
- Ccollections or groups of related data, shares the same set of attributes
14analytics
- Aprocess of gaining an understanding of data
- Bdiscipline of gaining an undestanding of data by analizing it via multitude techniques
- Ccollections or groups of related data
15in business-oriented, analytics
- Aresults can lower operational costs and facilitate strategic decison-making
- Bhelp identify the cause of a phenomenon to improve the accuracyof predictions
- Chelp strengthen the focus on delivering high quality services by driving down costs
16in the scientific domain, analytics
- Ahelp strengthen the focus on delivering high quality services by driving down costs
- Bresults can lower operational costs and facilitate strategic decison-making
- Chelp identify the cause of a phenomenon to improve the accuracyof predictions
17in services-based enviroments, analytics
- Aresults can lower operational costs and facilitate strategic decison-making
- Bhelp strengthen the focus on delivering high quality services by driving down costs
- Chelp identify the cause of a phenomenon to improve the accuracyof predictions
18Business intelligent
- Aprocess of gaining an understanding of data
- Bprocess of gaining insigths into the the workings of an enterprise
- Cdiscipline of gaining an undestanding of data by analizing it via multitude techniques
19KPI (Key performance indicators)
- Autilize the consolidated data contained in data warehouses to run analytical queries
- Bis a measure for gauging success within a particular context
- Cdiscipline of gaining an undestanding of data by analizing it via multitude techniques
20KPI used to
- Aachieve regulatory compliance
- Bhelp identify the cause of a phenomenon to improve the accuracyof predictions
- Chelp strengthen the focus on delivering high quality services by driving down costs
21measure in Big data
- Akilometer, megabyte, gigabyte, terabyte, petabyte, exabyte, settabytte, yottabyte
- Bkilobyte, megabyte, gigabyte, terabyte, petabyte, exabyte, settabytte, yottabyte
- Ckilobyte, megabyte, gigabyte, terabyte, petabyte, exabyte, settabytte, youtube
22big data emerged from a combination of business needs and technology innovations
- Atrue
- Bfalse
23analitcs & data science
- AFor many business, digital mediums have replaced physical mediums as the de facto
- BBased in opens sorce software tha requires little more than commodity hardware
- Cmachine learning algoritms, statistical techniques and data warehousing
24digitalization
- ALead to an opportunity to collect further secondary data
- BCollecting and storing more data to potentially find new insigths and gain a competitive edge
- Ccollecting and processing large quantities of diverse data has become increasingly affordable
25affordable technology & commodity hardware
- Athe madurity of these fields of practice inspired and enabled much of the core functionality
- Buse of commodity hardware makes the adoption of Big Data solutions accessible to business
- Csome examples include on-demand TV and streaming video
26social media
- AHas empowered customers to provide feedback in near-realtime via open and public mediums
- BHas resulted in massive data streams
- Care capable of providing highly scalable, on-demand IT resources that can be leased
27hyper-connected communities & devices
- AA an result, business are storing increasing amounts of data on customer interaction
- Bleverage the infrastructure, storage and processing capabilities provided by this enviroments
- Cthe broadening coverage of internet and the proliferation of cellar and WI-FI networks .
28cloud computing
- ABusinesses are also increasingly interested in incorporating publicly available datasets
- BIs either directly through online interaction or indirectly through the usage of connected devices
- CCan be leased dramatically reduces the required up-front investment of Big Data projects
29Online Transaction Processing (OLTP)
- Asoftware system that processes transaction-oriented data
- BIs a system used for processing data analysis queries
- Cprocess of loading data from a source system into a target system
30batch-processed
- AOLTP
- BOLAP
31data fully normalized
- AOLTP
- BOLAP
32C R U D with subsecond response times
- AOLTP
- BOLAP
33Online Analytical Processing (OLAP)
- Asoftware system that processes transaction-oriented data
- BStore operational data that is fully normalized
- CIs a system used for processing data analysis queries
34data mining and machine learning processes
- AOLTP
- BOLAP
35OLAP
- ARepresenting a common source of structured analytics input
- BCan serve as both a data source as well as a data sink that capable of receiving data
- CBig data analysis results can also be fed back
36OLAP
- ARepresenting a common source of structured analytics input
- Bare used in diagnostic, predictive and prescriptive analytics
- CAn example ticket reservation systems and banking and POS transactions
37data that is aggregated and denormalized
- AOLAP
- BOLTP
38OLAP use databases
- Athat store historical data in multidimensional arrays and can answer complex queries
- Bare comprised of simple insert, delete and update operations
- Cthat processes transaction-oriented data
39An OLAP system is always
- Afed with data from multiple OLTP system using regular batch processing jobs
- Bdata is fully normalized
- Ccomprised of simple C R U D
40OLAP: denormalized data in the form of cubes
- AFALSE
- BTRUE
41Extract-transform-load (ETL)
- Aallows the data to be queried during any data analysis tasks that are performes later
- BIs a process of loading data from a source system into a target system
- Cqueries can take several minutes or even longer, depending on the complexity of query.
42Extract-transform-load (ETL) source
- Adatabase, flat file or an application
- Bon-demand TV and streaming video
- Cdigitalization and social media
43ETL
- ARepresents the main operation through wich data warehouses are fed data
- BRepresents the main operation through wich datasets are fed data
- CRepresents de main operation through wich database are fed data
44ETL
- AExtract load transform
- BExtract transform load
- CExtract transform leave
45ETL type data
- AUnstructure data, structure data and semi structura data
- BOnly structure data and semi structura data
- COnly Unstructure data
46Data Warehouses EDWH has historical data?
- AFALSE
- BTRUE
47Data Warehouses EDWH
- Ais a subset of the data, that typically belongs to a deparment
- Bis a framework open source
- Cusually interface with an OLAP sYstem to support analytical queries
48EDWH
- Athis allows the data to be queried during any data analysis
- BHeavily used by BI to run various analytical queries
- Csoftware system that processes transaction-orientes data
49EDWH sources
- Asocial media, facebook twitter
- BOLTP, ERP, CRM and SCM systems
- COLAP, ERP, CRM and SCM systems
50EDWH
- AFor the amount data contained will continue to increase. The anlysis BI can suffer.
- BIs a process of loading data from a source system into a target system
- Csoftware system that processes transaction-orientes data
51EDWH
- AHas established itself as a de facto industry platform for contemporary Big Data solutions
- Busually contain optimized databases, called analytical databases to handle reporting and data analysis
- CRepresents de main operation through wich database are fed data
52EDWH: analytical database can´t exist as separate DBMS
- ATRUE
- BFALSE
53Data mart
- Ais a subset of the data, that typically belongs to a deparment
- Bcan have multiple EDWH
- Cbased on cleansed data, which is a prerequisite for accurate and error-free reports
54hadoop is open source framework for
- Alarge data storage
- Bdata processing
- Cdiagnostic, predictive and prescriptive
- Drun on commodity hardware
- Edenormalized data in the form of cubes
55hadoop
- Ahas established itself as a de facto industry platform for contemporary Big Data solutions
- Bis a central, enterprise-wide repository
- Cis always fed with data from multiple OLTP system using regular batch processing jobs
56hadoop can be used as engine of
- AETL
- Banalytics
- COLTP
- DEDWH
57hadoop can process large amounts of structured, semi-structured and unstructured data
- Afalse
- Btrue
58volumen refers to
- Ainsert data
- Bprocess data
- Cvelocity processing
59Data volumes can include
- AOnline transaction
- Bsensor data
- Cbatch
- Dsocial media
- EOLAP
- Fscientific and research data
60velocity
- Amultiple types of data that need to be supported by Big Data solutions
- Bdata translates into the amount of time it takes for the data to be processed
- Cdata is processed by Big Data solutions is substantial and usually ever growing
61Depending on the data source, velocity may not always be high
- Afalse
- Btrue
62variety
- AQuality or fidelity of data
- Busefulness of data for an enterprise
- Crefers to the multiple formats and types of data that need to be supported by Big Data Solutions
63veracity
- AThe appropriate form of data storage
- BRefers to the quality or fidelity of data
- CRefers ti the usefulness of data for an enterprise
64Noise and SIgnal refers to
- Avolumen
- Bvalue
- Cveracityx
65value
- Arefers to quality or fidelity of data
- Brefers to the multiple formats and types of data that need to be supported
- Crefers to usefulness of data for an enterprise
66the value is directly related to the veracity in that de higher the data fidelity, the more value it holds for the business.
- Atrue
- Bfalse
67type of data
- Astructured data, unstructured data, semi-structured data
- Bstructured data, unstructured data, semi-structured data, metadata
68ERP and CRM are example of
- Aunstructured data
- Bstructured data
- Csemi-structured data
69image, audio and video files are examples of
- Asemi-structured data
- Bunstructured data
- Cstrutured data
70Unstructured data generally makes up 80%
- Atrue
- Bfalse
71unstructured data does generally require special or customized logic when it comes to pre-processing and storage
- Atrue
- Bfalse
72semi-structured data
- Ahas a defined level of structured and consistency can be relational in nature
- Bhas a defined level of structured and consistenc, but cannot be relational in nature
- Ccannot be inheremtly processed or queried using SQL or traditional programming features
73semi_structured data
- ACRM or ERP
- BXML or , electronic data interchanges, e-mails, spreaddheets, RSS feeds and senso data
- Cimage or adio files
74metadata
- Aprovides information about the analysis
- Bprovides information about a datasets characteristics and structure
75metadata generally machine generated and utomatically appended to the data
- Atrue
- Bfalse
76metadata xml tag
- Aprovided the author and creation date of a document, file size and resolution of a digital photograph
- Baudio binary file
- Cvideobinary file
77semi-structured data and unstructured data have a greater noise-to-signal ratio than structured data
- Atrue
- Bfalse
78can ETL can cleansing data and verification
- Afalse
- Btrue
79data analysis
- Aquantitative analysis
- Bcientics analysis
- Cqualitative analysis
- Ddata mining
80quantitative analysis
- Aquantifying analysis the patterns and correlations found in the data
- Bphenomrnon in the data
- Coutlayer
81qualitative analysis use
- Anumbers
- Bwords
- Cgraphical
82involve analyzing a smaller sample in greater depth
- Aquantitave analysis
- Bqualitative analysis
83analysis that targets large datasets
- Aquantitative analysis
- Bdata mining
- Cqualitative analysis
84data mining (data discovery)
- Adescriptions
- Bpatterns and correlations
- Cidentify patterns and trends
85data mining forms the basis for predictive analytics and business intelligence (BI)
- Afalse
- Btrue
86analysis tools can automate data analyses
- Afalse
- Btrue
87descriptive , diagnostic , predictive , prescriptive
- Atypes of analytics
- Btypes of analisys
- Cdiagnostic
88questions about events that have already occurred
- Adiagnostic analytics
- Bdescriptive analytics
- Cpredictive analytics
89reporting or dahsboards. The reports are generally static, queries are executed in OLTP such CRM and ERP
- Adescriptive analytis
- Bdiagnostic analytics
- Cpreescriptive analytics
90determine the causes of a phenomenon that occurred in the past
- Adescriptive analytics
- Bdiagnostic analytics
- Cpredictive analytics
91interactive visualization to identify trends and patterns, and queries are executed in OLAP systems
- Adescriptive analytics
- Bdiagnostic analytics
- Cpreescriptive analytics
92attemp to determine the outcome of an event that might occur in the future
- Apreescriptive analytics
- Bpredictive analytics
- Cdescriptive analytics
93The focus is on which prescribed option to follow and why and when it should be followed, to gain and advantage or mitigate risk
- Adescriptive analytics
- Bprescriptive analytics
- Cpredictive analytics
94incorporate internal data (historical etc..) and external data (social media, demographic data)
- Adescriptive analytics
- Bprescriptive analytics
- Cdiagnostic analytics
95machine learning
- Ais the process of teaching computers to learn from existing data and apply the acquired knowledge to formulate predictions about unknow data.
- Bis the discipline of teaching computers to learn from existing data and apply the acquired knowledge to formulate predictions about unknow data.
- Cis the framework refers to teaching computers to learn from existing data and apply the acquired knowledge to formulate predictions about unknow data.
96based on the input data and categorys
- Asupervised learning
- Bunsupervised learning
97the algorithm attemp to categorized data by grouping data with similara attributes together
- Asupervised learning
- Bunsupervised learning
98machine learning makes predictions and identify hidden patterns
- Afalse
- Btrue
99machine learning can use the output from data mining for further data classification.
- Atrue
- Bfalse
100traditional BI utilizes
- Adescriptive and diagnostic
- Bdiagnostic and predictive
- Cdescriptive an prescriptive
101ad-hoc reports and dashboards
- Atradictional Big Data
- Btraditional BI
102ad hoc reporting
- Athe focus is a usually on a specific area of the bussines
- Bthe focus is view of key bussines areas in real time or near to real time
103big data
- Afacilitate the development of an enterprise-wide understanding of the way a bussines works
- Bfocus on indivicual bussines processes
- Cdescriptive and diagnostic to facilitate the development of an an enterprise-wide understanding of the way a bussines works
104data visualization analytical results are graphically communicated using elements like
- Acharts, maps, data grids, infographics and alerts
- Bad-hoc reports drill, down
- Cdashboards
105data visualization tool in Big Data use
- Ain-disk analytical tchnologies
- Bin-memory analytical technologies
106aggregation
- Agroups data across multiple categories to show subtotals and totals
- Bglobal an sumarized view of data across multiple context
- Cenables a detail view of the data of interest by focusing in on a data subset
107visualization features: drill down
- Aenables a detail view of the data of interest by focusing in on a data subset
- Bglobal an sumarized view of data across multiple context
- Cgroups data across multiple categories to show subtotals and totals
108visualization features: roll up
- Aglobal an sumarized view of data across multiple context
- Bgroups data across multiple categories to show subtotals and totals
- Cenables a detail view of the data of interest by focusing in on a data subset
109data visualization tools for Big data solutions incorporate
- Adiagnostic and descriptive
- Bpredictive a descriptive
- Cpredictive and prescriptive
110in the advance visualizations you needs an ETL
- Atrue
- Bfalse
Información
- Descripción
- test rápido y para repaso
- Autor
- Carolina Colorado
- Idioma
- Español
- Visitas
- 67
- Etiquetas
- Valoración
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