Data Analytics MCQ Unit-I

UNIT- I

 1.   _______________ is high volume, high velocity and high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization.

[A]      Data mining

[B]      Big data

[C]      Data warehouse

[D]     Business Intelligence

2.   The important 3V’s in big data are_______________________.

[A]      volume, vulnerability,variety

[B]      volume, velocity and variety

[C]      variety,vulnerability,volume

[D]     velocity,vulnerability,variety

 

3.   ________ refers to the evolving types and growing sources of data, including semi-structured and unstructured data.

[A]      Velocity

[B]      Variety

[C]      Volume

[D]     Value

4.   _________ in the context of big data refers to the speed of data acquisition and processing.

[A]      Variety

[B]      Volume

[C]      Velocity

[D]     Value

 

5.   Which of these is NOT one of the 4 V’s?

[A]      Volume

[B]      Velocity

[C]      Variety

[D]     Voice

6.   Big data is an evolving term that describes any voluminous amount of data that has the potential to be mined for information.

[A]      Structured

[B]      Semi-structured

[C]      Unstructured

[D]     All the above

 

7.   Variety refers to ______________.

[A]      structured data

[B]      unstructured data

[C]      semi-structured data

[D]     all the above

 

 8.   Which of the following term is appropriate to data that involve volume, variety, velocity?

[A]      Large Data

[B]      Big Data

[C]      Dark Data

[D]     None of the above

 

9.   Which of the following is not a major data analysis approaches?

[A]      Data Mining

[B]      Predictive Intelligence

[C]      Business Intelligence

[D]     Text Analytics

 

10.  _______________ concerns all data which can be stored in database SQL in table with rows and columns

[A]      Semi-structured data

[B]      Unstructured data

[C]      Structured data

[D]     None of the above

 

11.  _______________ is information that doesn’t reside in a relational database but that does have some organizational properties that make it easier to analyze.

[A]   Structured data

[B]   Unstructured data

[C]   Semi-structured data

[D]  None of the above

12.  _________ refers to information that either does not have a predefined data model or is not organized in a predefined manner.

[A]   Structured data

[B]   Semi-structured data

[C]   Unstructured data

[D]  None of the above

 

 13.  ___________Is the discovery and communication of meaningful patterns in data.

[A]   Structured data

[B]   Semi-structured data

[C]   Analytics

[D]  None of the above

 

14.  Analytics often favors ____________ to communicate insight.

[A]   Data Cleansing

[B]   Data Integration

[C]   Data Replication

[D]  Data visualization

 

15.  Point out the wrong statement.

[A]   The big volume indeed represents Big Data

[B]   The data growth and social media explosion have changed how we look at the data

[C]   Big Data is just about lots of data

[D]  All of the mentioned

16.  Data Analysis is a process of?

[A]   inspecting data

[B]   cleaning data

[C]   transforming data

[D]  All of the above

17.  Which of these is NOT a type of Analytics?

[A]   Predictive Analytics

[B]   Result Analytics

[C]   Prescriptive Analytics

[D]  Descriptive Analytics

18.  ____________ uses data to determine the probable future outcome of an event or a likelihood of a situation occurring.

[A]   Descriptive analytics

[B]   Prescriptive analytics

[C]   Predictive Analytics

[D]  None of the above

 19.  ___________ looks at past performance and understands that performance by mining historical data to look for the reasons behind past success or failure.

[A]  Descriptive analytics

[B]   Prescriptive analytics

[C]   Predictive Analytics

[D]  None of the above

20.  ___________ goes behind predicting future outcomes by also suggesting actions to benefit from the predictions and showing the decision maker the implications of each decision option.

[A]   Descriptive analytics

[B]   Prescriptive analytics

[C]   Predictive Analytics

[D]  None of the above

 

21.  ______________ refers to computer based techniques used in spotting, digging-out and analyzing business data.

[A]   Data mining

[B]   Big data

[C]   Data warehouse

[D]  Business Intelligence

 

22.  __________________ is defined as the capability that enables the mobile workforce to gain business insights through information analysis using applications optimized for mobile devices.

[A]   Data mining

[B]   Big data

[C]   Data warehouse

[D]  Mobile Business Intelligence

23.  __________ is the potential for a loss related to our data.

[A]   Cost risk

[B]   Schedule risk

[C]   Data risk

[D]  Performance risk

24.  Which of the following is type of data risk?

[A]   Data Security

[B]   Data Privacy

[C]   Bad data

[D]  All of the above

25.  Which of following is/are the Big Data Technologies

[A]   Operational Big Data Technologies

[B]   Analytical Big Data Technologies

[C]   Both of the above

[D]  None of the above

 26.  ___________ is a technique in which a network of remote servers is hosted on the Internet.

[A]   Data Analytics

[B]   Cloud Computing

[C]   Big Data

[D]  Data warehouse

27.  The big data are collected from a wide variety of sources.

[A]  True

[B]   False

[C]   Cannot say

[D]  Undefined

28.  Crowd sourcing involves obtaining work, information, or opinions from a large group of people who submit their data via ________________.

[A]   Internet          

[B]   social media        

[C]   smartphone apps        

[D]  all of the above

 29.  Cloud Computing is ________ of data analytics

[A]   dependent

[B]   independent

[C]   proportional

[D]  None of the above

30.  Big data is used to uncover___________________________.

[A]   hidden patterns&unknown correlations

[B]   market trends & customer preferences

[C]   other useful information

[D]  all the above

Data Analytics MCQ Unit-II

 

UNIT- II

 

1.   _____________ is an interdisciplinary field of scientific methods, process and systems to extract knowledge or insights from data in various forms, either structured or unstructured.

[A]      Data mining
[B]      Data science
[C]      Data warehouse
[D]     None of the above

 

2.   _____________is the technology which uses the transformed and loaded historical data to get or create the reports.

[A]      Data mining
[B]      Big data
[C]      Business Intelligence
[D]     None of the above

 

3.   The process of conversion of data often through the use of scripting languages to make it easier to work with is known as _______________.

[A]      Big data
[B]      Business Intelligence
[C]      Data wrangling
[D]     None of the above

 

4.   A _____________ is a storage repository that holds a vast amount of raw data in its native format until it is needed and refined elsewhere.

[A]      Big data
[B]      Data lake
[C]      Data wrangling
[D]     None of the above

 

5.   In ________________ data is stored at the leaf level in an untransformed or nearly untransformed state.

[A]      Big data
[B]      Data lake
[C]      Data wrangling
[D]     None of the above

 

6.   In _______________ data is transformed and schema is applied to fulfill the needs of analysis.

[A]      Big data
[B]      Data lake
[C]      Data wrangling
[D]     None of the above

 

7.   A____________ is a database which is kept separate from the organization’s operational database

[A]      Big data
[B]      Data warehouse
[C]      Data wrangling
[D]     None of the above

 

8.   A _______________ is constructed by integrating data from multiple heterogeneous sources that support analytical reporting, structured and decision making.

[A]      Big data
[B]      Data warehouse
[C]      Data wrangling
[D]     None of the above

 

9.   The view over an operational data warehouse is known as a ________________.

[A]      data lake
[B]      virtual warehouse
[C]      data wrangling
[D]     None of the above

 

10.              ____________ contains a subset of organization-wide data

[A]      Data lake
[B]      Virtual warehouse
[C]      Data Mart
[D]     None of the above

 

11.  An_________________ collects all the information and the subjects spanning an entire organization.

[A]      Data lake
[B]      enterprise warehouse
[C]      Data wrangling
[D]     None of the above

 

12.  ______________ involves gathering data from multiple heterogeneous sources.

[A]      Refreshing
[B]      Data extraction
[C]      Data cleaning
[D]     None of the above

 

13.  ______________ involves finding and correcting the errors in data

[A]      Data extraction
[B]      Data cleaning
[C]      Data transformation
[D]     None of the above

 

 

14.  ______________ involves converting the data from legacy format to warehouse format.

[A]      Data extraction
[B]      Data cleaning
[C]      Data transformation
[D]     None of the above

 

15.  ______________ involves sorting, summarizing, consolidating, checking integrity and building indices and partitions.

[A]      Data extraction
[B]      Data loading
[C]      Data transformation
[D]     None of the above

 

16.  ______________ involves updating from data sources to warehouse.

[A]   Data extraction
[B]   Data cleaning
[C]   Refreshing
[D]  None of the above

 

17.  ____________ describes a resource for purposes such as discovery and identification.

[A]   Structural metadata
[B]   Administrative metadata
[C]   Descriptive metadata
[D]  None of the above

 

18.  ____________ indicates how compound objects are put together.

[A]  Structural metadata
[B]   Administrative metadata
[C]   Descriptive metadata
[D]  None of the above

 

19.  ____________ provides information to help manage a resource.

[A]   Structural metadata
[B]   Administrative metadata
[C]   Descriptive metadata
[D]  None of the above

 

20.  ____________ contains the data ownership information, business definition and changing policies.

[A]   Operational metadata
[B]   Administrative metadata
[C]   Business metadata
[D]  None of the above

 

21.  ____________ includes currency of data and data lineage.

[A]   Administrative metadata
[B]   Operational metadata
[C]   Business metadata
[D]  None of the above

 

22.  ____________ includes dimension algorithms, data on granularity, aggregation and summarizing.

[A]   Administrative metadata
[B]   Operational metadata
[C]   The algorithms for summarization
[D]  None of the above

 

23.  _______ is an agile, iterative data science methodology to deliver predictive analytics solution and intelligent applications efficiently.

[A]   ASP
[B]   PSP
[C]   DSP
[D]  None of the above

 

24.  _____________ is an environment for building scalable machine learning algorithms.

[A]   Python
[B]   Apache Mahout
[C]   SQL
[D]  None of the above

 

25.  _____________ is a cluster-computing framework for data analysis.

[A]  Apache Spark
[B]   Python
[C]   SQL

[D]  None of the above

 

26.  _____________ is the massive parallel processing database for Apache Hadoop.

[A]   Python
[B]   SQL
[C]   Impala
[D]  None of the above

 

27.  _____________ is a computational platform for real-time analytics,

[A]   Python
[B]   Apache Storm
[C]   SQL
[D]  None of the above

 

28.  _____________ is a NoSQL database known for its scalability and high performance.

[A]   Python
[B]   SQL
[C]   MongoDB
[D]  None of the above

 

29.  _____________ is a JavaScript library for building interactive data visualization within your browser.

[A]   Apache Spark
[B]   SQL
[C]   D3
[D]  None of the above

 

30.  _____________ is the product of Google’s Brain Team coming together for the purpose of advancing machine learning.

[A]   Apache Spark
[B]   SQL
[C]   Tensor Flow

[D]  None of the above

Data Analytics MCQ Unit-IV

 

UNIT- IV

 

1.      __________ is a general term that describes any effort to help people understand the significance of data by placing it in a visual context.

[A]   Data attribute

[B]   Data visualization

[C]   Both of the above

[D]  None of the above

 

2.      Data visualization is the ___________ representation of information and data.

[A]   Logical

[B]   Graphical

[C]   Text

[D]  Visual

 

3.      Data visualization tools provide an accessible way_____________________________.

[A]   To see and understand trends

[B]   To outliers

[C]   To patterns in data.

[D]  All Above

 

4.      Data Science is/are ________________.

[A]   Art

[B]   Science

[C]   Both art and science

[D]  Technique

 

5.      Data attributes is/are___________________.

[A]   Quantitative

[B]   Qualitative

[C]   Both quantities and qualitative

[D]  None of above 

 

6.      Quantitative data cannot take shape_____________________________________.

[A]   Ratio

[B]   Data on which perform arithmetic operations

[C]   Interval

[D]  Set of data on which perform arithmetic operations

 

7.      When time series is used?

[A]  A single variable is captured over period of time.

[B]   Categorical subdivision is ranked in ascending or descending order.

[C]   Categorical subdivision is compared against reference.

[D]  Comparing categorical subdivision in no particular order.

 

8.      When nominal comparison is used?

[A]   A single variable is captured over period of time.

[B]   Categorical subdivisions are ranked in ascending or descending order.

[C]   Categorical subdivision is compared against reference.

[D]  Comparing categorical subdivision in no particular order.

 

9.      When ranking is used?

[A]   A single variable is captured over period of time.

[B]   Categorical subdivisions are ranked in ascending or descending order.

[C]   Categorical subdivision is compared against reference.

[D]  Comparing categorical subdivision in no particular order.

 

10.  When deviation is used?

[A]   A single variable is captured over period of time.

[B]   Categorical subdivisions are ranked in ascending or descending order.

[C]   Categorical subdivision is compared against reference.

[D]  Comparing categorical subdivision in no particular order.

 

11.  When geospatial used?

[A]   A single variable is captured over period of time.

[B]   Categorical subdivisions are ranked in ascending or descending order.

[C]   Categorical subdivision is compared against reference.

[D]  None of above

 

12.  Qualitative data is/are____________________________.

[A]   Ordinal

[B]   Data with fixed ranking

[C]   Ordinal and Data with fixed ranking

[D]  Neither ordinal nor data with fixed ranking

 

13.  The steps for Interactive Visualization are_____________________.

[A]   Selecting, Filtering 

[B]   Selecting, Linking, Filtering, Rearranging 

[C]   Linking, Rearranging 

[D]  None of the above

 

14.  __________ is useful for relating information among multiple views.

[A]   Rearranging

[B]   Selecting

[C]   Linking

[D]  None of the above

 

15.  __________ helps users adjust the amount of information for display.

[A]   Rearranging

[B]   Filtering

[C]   Linking

[D]  None of the above

 

16.  For the visual encoding which variable is/are used?

[A]   Planer variable

[B]   Retinal variable

[C]   Both planner and retinal variable

[D]  Data variable 

 

17.  How many planer variables for visualisation?

[A]   7

[B]   5

[C]   2

[D]  None of the above

 

18.  How many retinal variables?

[A]  5

[B]   4

[C]   3

[D]  2

 

19.  Good Visual design based on

[A]   Psychology

[B]   Psychophysics

[C]   Psychology and Psychophysics

[D]  General Knowledge and Common sense

 

20.  __________ refers to making machines intelligent in a wide array of activities that involve thinking and reasoning.

[A]   Narrow AI

[B]   General AI

[C]   Both of the above

[D]  None of the above

 

21.  __________ involves the use of artificial intelligence for a very specific task.

[A]  Narrow AI

[B]   General AI

[C]   Both of the above

[D]  None of the above

 

22.  ____________ are applications that allow users to tap into the capabilities of their devices to automate their lives.

[A]  SmartApps

[B]   Data mining

[C]   Data warehouse

[D]  None of the above

 

23.  __________ use AI and machine learning to interact in a more intelligent way with people and surroundings.

[A]   Blockchain

[B]   Intelligent of Things (IOT)

[C]   Both of the above

[D]  None of the above

 

24.  A digital twin is a____________________________________________________.

[A]  Virtual representation that serves as the real-time digital counterpart of a physical object or process. 

[B]   Real representation that serves as the real-time digital counterpart of a physical object or process. 

[C]   Virtual representation that serves as the past digital counterpart of a physical object or process. 

[D]  Real representation that serves as the past digital counterpart of a physical object or process. 

 

25.  __________ can be used in the automobile sector for creating virtual model of a connected vehicle.

[A]   Blockchain

[B]   Digital Twins

[C]   Both of the above

[D]  None of the above

 

26.  Virtual reality is ___________________________________.

[A]   a perception of being physically present in a physical world.

[B]   a perception of being physically present in a non-physical world.

[C]   a perception of being logically present in a non-physical world.

[D]  a perception of being logically  present in a physical world.

 

27.   Augmented reality is used____________________________________.

[A]   To enhance virtual environments or situations and offer perceptually enriched experiences.
[B]   To enhance metal environments or situations and offer perceptually enriched experiences.
[C]   To enhance natural environments or situations and offer perceptually enriched experiences.
[D]  To enhance Psychological environments or situations and offer perceptually enriched experiences.

 

28.  Edge computing is _______________________.

[A]  Distributed computing

[B]   Time sharing computing

[C]   Real time computing

[D]  Offline Computing

 

29.  __________ describes a computing topology in which information processing and content collection and delivery are placed closer to the sources of this information.

[A]   Blockchain

[B]   Edge Computing

[C]   Both of the above

[D]  None of the above

 

30.  A blockchain is a type of?

[A]   Object

[B]   Database

[C]   Table

[D]  View

 

31.  A blockchain originally block chain, is a continuously growing list of records called block, which are linked and secured using ________________.

[A]   hash

[B]   Cryptogarphy

[C]   Both of the above

[D]  None of the above

 

32.  Key issues in big data visualization are_______________________________.

[A]   Availability of visualization specialists

[B]   Visualization hardware resources

[C]   Data quality

[D]  All of above

 

33.   Preattentive attributes are ___________________________________.

[A]   Natural properties that we notice without using conscious effort to do so.

[B]   Visual properties that notice without using conscious effort to do so.

[C]   Imaginary properties that we notice without using conscious effort to do so.

[D]  General properties that we notice without using conscious effort to do so.

 

34.  Which statement false about correlation matrix.

[A]   A correlation matrix is a table showing correlation coefficients between variables.

[B]   Each cell in the table shows the correlation between two variables.

[C]   A correlation matrix is not used to summarize data.

[D]  Correlation matrix Summarize data as an input into a more advanced analysis.