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