Test
data analytics midterm
Este test tiene tipos de pregunta que se abren en el reproductor de siempre.
Preguntas del test (71)
1In which version of BI, we have collaboration focus, self-regulated and real-time process?
- A1.0
- B2.0
- C3.0
- DNone of the above
2which one is not in the data science life cycle?
- ACapture
- BMaintain
- CAnalyze
- DAll of them are part of the data science life cycle
3Which one is not part of OLAP?
- ASelect
- BRollup
- CDrill down
- Dslice & Dice
4What is the definition of Roll up in multi-dimensional databases?
- AIt will be used when we want to pick up a small part of the cube.
- BIt can help users navigate through the data details.
- CGet overall information not in detail and refers to data aggregation and computation in one or more dimensions.
- DIt will be used when cutting the cube for example upper slides of it.
5The problem of finding hidden structures in unlabeled data is called:
- ACandidate Generation
- BSupervised learning
- CUnsupervised learning
- DNone of the above
6In a short sentence please describe what is data mining?
The analysis of observational data sets to find unsuspected relationships and to summarize the data in ways that are both understandable and useful- AVerdadero
- BFalso
7In your opinion, what is Business Intelligence?
An Umbrella term that includes the applications, infrastructure and tools and best practices that enable access and analysis of information to improve and optimize decisions and performance- AVerdadero
- BFalso
8Please provide three applications in data mining?
- AFraud Detection
- BDirect Marketing
- CEmail Classification
9 Please provide the five steps in Knowledge Discovery Database or KDD?
- ASelection
- BPreprocessing
- CTransformation
- Ddata mining
- EInterpretation
10A clustering algorithm finds and groups data points with natural similarities
- AVerdadero
- BFalso
11Median: middle value of a set
Mode: most frequent
Range: max - min
Quartiles:
1. order the set
2. split set in half, then each half into another half
3. each split represents Q1 to Q3
Interquartile range:
1. do the above
2. calculate: Q3 - Q1- AVerdadero
- BFalso
12Explain the difference between independent data marts and Data mart bus architecture?
Dependent data marts draw data from a central data warehouse that has already been created
Independent data marts are standalone systems built by drawing data directly from operational or external sources of data or both- AVerdadero
- BFalso
13The data is stored, retrieved & updated in ____________.
- AOLAP
- BSMTP
- COLTP
- DFTP
14The full form of OLAP is ....
- AOnline Advanced Processing
- BOffline Advanced Processing
- COnline Analytical Performance
- DOnline Analytical Processing
15 An ________ system is market-oriented and is used for data analysis by knowledge workers, including managers, executives, and analysts.
- AOLAP
- BOLTP
- CNone of the above
- DBoth of the above
16What do data warehouses support?
- AOLAP
- BOLTP
- COLAP and OLTP
- DOperational databases
17It is because of ________________ that enables OLAP to achieve great performance for a query.
- AComposition
- BDice
- CHybrid
- DAggregation
18What are the applications of OLAP?
- Abusiness reporting for sales
- BBudgeting
- CForecasting
- DAll of the above
19The process of viewing the cross-tab (Single dimensional) with a fixed value of one attribute is...
- ABoth Slicing and Dicing
- BSlicing
- CDicing
- DPivoting
20A data cube consists of
- AAnalytical data
- BDimensional data
- CMultidimensional data
- DProcedural data
21The operation of moving from finer-granularity data to a coarser granularity (by means of aggregation) is called a ________
- ADrill down
- BDicing
- CRollup
- DPivoting
22Data that can be modeled as dimension attributes and measure attributes are called _______ data.
- AMeasured
- BSingle-dimensional
- CMultidimensional
- DDimensional
23What is the main goal of data mining?
- Ao store and distribute data
- BTo turn raw data into helpful information
- CTo classify data for further tasks
24Which of the following is not an application of data mining?
- AFinancial fraud detection
- BSpam e-mail filtering
- CCost minimization
25A supermarket wants to divide their customers into different groups with distinctive features. Which process should be used?
- AUnsupervised learning
- BSupervised learning
- CReinforcement learning
26What does ETL stand for?
- AExclude, transport, load
- BExtract, transform, load
- CExtract, transport, lift
27The problem of finding hidden structures in unlabeled data is called...
- ASupervised learning
- BUnsupervised learning
- CReinforcement learning
28Main data mining task
- AClassification
- BAssociation rule mining
- CClustering
- DData visualization
29List three of the terms that have been predecessors of analytics.
- Adata support systems (DSS)
- Boperations research (OR)
- Cexpert systems (ES)
30Did DSS evolve into BI or vice versa?
- ADSS evolved into BI
- BBI evolved into DSS
31List and describe the major components of BI.
- Adata warehouse
- Bbusiness analytics
- Cbusiness performance management
- Duser interface
32Define analytics.
analytics is the process of developing actionable decisions or recommendations for actions based upon insights generated from historical data- AVerdadero
- BFalso
33What is descriptive analytics?
Descriptive analytics refers to knowing what is happening in the organization and understanding some underlying trends and causes of such occurrences- AVerdadero
- BFalso
34How is descriptive analytics different from traditional reporting?
Descriptive analytics gathers more data, often automatically- AVerdadero
- BFalso
35[blank_start]predictive analytics[blank_end] - use of statistical techniques and data mining to determine what is likely to happen in the future
[blank_start]prescriptive analytics[blank_end] - set of techniques that use descriptive data and forecasts to identify the decisions most likely to result in the best performance- Apredictive analytics
- Bprescriptive analytics
36What are some of the most common metrics that make for analytics-ready data?
- AData source reliability
- BData content accuracy
- CData accessibility
- DData security
37How do you describe the importance of data in analytics?
Can we think of analytics without data?
data is the raw material for analytic. without data there would be no analytics- AVerdadero
- BFalso
38What are the main categories or taxonomy of data?
Structured data and Unstructured data- AVerdadero
- BFalso
39What does it mean to clean/scrub the data?
values in the data set are identified and dealt with.
missing values need to be imputed or ignored when anomaly- AVerdadero
- BFalso
40What are some common tasks performed in data preprocessing?
- AData consolidation
- Bdata cleaning
- Cdata transformation
- Ddata reduction
41What does a box-and-whiskers plot show you?
show whether data is symmetrically distributed with respect to the mean or it sways one way or another.- AVerdadero
- BFalso
42What are the commonalities and differences between regression and correlation?
Both attempt to describe the association between two(or more) variables
correlation gives an estimate on the degree of association between variables
regression attempts to describe dependence of a response variable on one(or more) explanatory variables- AVerdadero
- BFalso
43P-value in regression model:
If p-value is less than 0.01 then results are highly significant.
If p-value is between 0.01 and 0.05 then results are statistically significant.
If p-value is between 0.05 and 0.1 then results are tending towards significance.
If p-value is greater than 0.1 then results are not statistically significant- AVerdadero
- BFalso
44What are the most commonly pronounced assumptions for linear regression?
- ALinearity
- BIndependence
- CNormality
- DConstant variance
- EMulticollinearity
45What is a data warehouse?
a pool of data produced to support decision making- AVerdadero
- BFalso
46How does a data warehouse differ from a database?
Technically a data warehouse is a database, however it is an integrated repository of detail used for decision support
databases are highly normalized
Data warehouses are highly denormalized for performance reasons- AVerdadero
- BFalso
47What is an ODS?
Operational Data Store is the database from which a business operates on an on-going basis- AVerdadero
- BFalso
48Explain the importance of metadata.
Metadata, "data about data," are the means through which applications and users access the content of a data warehouse- AVerdadero
- BFalso
49Describe the data warehousing process
1. Data are imported
2. Data are cleansed and organized
3. Data are loaded into the enterprise data warehouse, or Data mart
4. Analyses are performed- AVerdadero
- BFalso
50List the benefits of data warehouses.
- AAllowing end users to perform extensive analysis
- BA consolidated view of corporate data
- CBetter and more timely information
- DEnhanced system performance
- ESimplification of data access
51Issues to consider when deciding which architecture to use for data warehouse:
- which database management system to use
- will parallel processing be used
- will data migration tolls be used to load the data
- what tools will be used to support data retrieval- AVerdadero
- BFalso
52What is OLAP and how does it differ from OLTP?
Data stored in a data warehouse can be analyzed using techniques referred to as OLAP
OLAP is one of the most commonly used data analysis techniques
OLTP is concerned with the capture and storage of data, as well as data analysis- AVerdadero
- BFalso
53What is a cube in data structure?
A cube in OLAP is a multidimensional data structure that allows fast analysis of data- AVerdadero
- BFalso
54What do drill down, roll up, and slice and dice mean?
[blank_start]Slice[blank_end]: a subset of a multidimensional array corresponding to a single value set for one (or more) of the dimensions not in the subset.
[blank_start]Dice[blank_end]: a slice on more than two dimensions of a data cube.
[blank_start]Drill Down/Up[blank_end]: specific OLAP technique whereby the user navigates among levels of data ranging from the most summarized (up) to the most detailed (down).
[blank_start]Roll-up[blank_end]: involves computing all the data relationships for one or more dimension- ASlice
- BDice
- CDrill Down/Up
- DRoll-up
55SQL AGGREGATE
SELECT SUM(column) FROM TABLE WHERE <condition>- AVerdadero
- BFalso
56SQL ROLLUP
SELECT <columns> FROM TABLE GROUP BY ROLLUP(columns)- AVerdadero
- BFalso
57SQL ROLLDOWN
SELECT <columns> FROM TABLE GROUP BY ROLLDOWN(columns)- AVerdadero
- BFalso
58SQL SLICING
SELECT <columns> FROM TABLE WHERE <condition> GROUP BY column- AVerdadero
- BFalso
59SQL DICING
SELECT <columns> FROM TABLE WHERE <condition> AND <condition> GROUP BY column- AVerdadero
- BFalso
60SQL JOIN
SELECT <columns> FROM TABLE INNER JOIN TABLE ON <condition>
INNER JOIN
OUTER JOIN
LEFT JOIN
RIGHT JOIN- AVerdadero
- BFalso
61What is scalability? How does it apply to data warehouses?
Scalability refers to the degree to which a system can adjust to changes in demand without major additional changes or investments
Data warehouses scalability issues are the amount of data in the warehouse, how quickly the warehouse is expected to grow, the number of concurrent users, and the complexity of user queries- AVerdadero
- BFalso
62Define data mining
Data mining is the process through which previously unknown patterns in data were discovered- AVerdadero
- BFalso
63What are some major data mining methods and algorithms?
prediction
association
clustering- AVerdadero
- BFalso
64How can data mining be used to fight terrorism?
detect money laundering and other forms of terrorist financing- AVerdadero
- BFalso
65Do you think data mining, while essential for fighting terrorist cells, also jeopardizes individuals' rights of privacy?
Yes, because it inevitably involves tracking personal and financial data of individuals.- AVerdadero
- BFalso
66Why do we need a standardized data mining process? What are the most commonly used data mining processes?
In order to systematically carry out data mining projects, a general process is usually followed.
commonly used: CRISP-DM, SEMMA, and KDD.- AVerdadero
- BFalso
67Discuss the differences between the two most commonly used data mining process.
CRISP-DM takes a more comprehensive approach—including understanding of the business and the relevant data
SEMMA implicitly assumes that the data mining project's goals and objectives along with the appropriate data sources have been identified and understood.- AVerdadero
- BFalso
68Are data mining processes a mere sequential set of activities?
No. Even though these steps are sequential in nature, there is usually a great deal of backtracking- AVerdadero
- BFalso
69What is the main difference between classification and clustering?
Classification learns patterns from past data
Cluster analysis is an exploratory data analysis tool for solving classification problems- AVerdadero
- BFalso
70Data mining mistakes
- ASelecting the wrong problem for data mining
- BBeginning without the end in mind
- CNot leaving sufficient time for data acquisition, selection and preparation
- DLooking only at aggregated results and not individual records
71Association rule mining
also known as market basket analysis
finds interesting relationships between variables- AVerdadero
- BFalso
Información
- Descripción
- Computer Science Test sobre data analytics midterm, creado por Upma Sharma el 02/11/2021.
- Autor
- Upma Sharma
- Idioma
- English
- Visitas
- 0
- Etiquetas
- Valoración
- Aún sin valoraciones Valora este test