Data mining and warehousing mcq questions

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Data mining and warehousing mcq

1. What is true about data mining?

  1. Data Mining is defined as the procedure of extracting information from huge sets of data
  2. Data mining also involves other processes such as Data Cleaning, Data Integration, Data Transformation
  3. Data mining is the procedure of mining knowledge from data.
  4. All of the above
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All of the above

2. How many categories of functions involved in Data Mining?

  1. 1
  2. 2
  3. 3
  4. 4
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2

3. The mapping or classification of a class with some predefined group or class is known
as?

  1. Data Characterization
  2. Data Discrimination
  3. Data Set
  4. Data Sub Structure
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Data Discrimination

4. The analysis performed to uncover interesting statistical correlations between
associated-attribute-value pairs is called?

  1. Mining of Association
  2. Mining of Clusters
  3. Mining of Correlations
  4. None of the above
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Mining of Correlations

5. ______ may be defined as the data objects that do not comply with the general
behavior or model of the data available.

  1. Outlier Analysis
  2. Evolution Analysis
  3. Prediction
  4. Classification
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Outlier Analysis

6. “Efficiency and scalability of data mining algorithms” issues comes under?

A. Mining Methodology and User Interaction Issues
B. Performance Issues
C. Diverse Data Types Issues
D. None of the above

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Performance Issues

7. To integrate heterogeneous databases, how many approaches are there in Data
Warehousing?

  1. 1
  2. 2
  3. 3
  4. 4

2

8. Which of the following is correct advantage of Update-Driven Approach in Data
Warehousing?

A. This approach provides high performance.
B. The data can be copied, processed, integrated, annotated, summarized and
restructured in the semantic data store in advance.
C. Both A and B
D. None Of the above

Both A and B

9. What is the use of data cleaning?

A. to remove the noisy data
B. correct the inconsistencies in data
C. transformations to correct the wrong data.
D. All of the above

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All of the above

10. Data Mining System Classification consists of?

A. Database Technology
B. Machine Learning
C. Information Science
D. All of the above

All of the above

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11. Which of the following is a good alternative to the star schema?

  1. snow flake schema
  2. star schema
  3. star snow flake schema
  4. fact constellation
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fact constellation

12. Patterns that can be discovered from a given database are which type…

  1. More than one type
  2. Multiple type always
  3. One type only
  4. No specific type

More than one type

13. Background knowledge is…

  1. It is a form of automatic learning.
  2. A neural network that makes use of a hidden layer
  3. The additional acquaintance used by a learning algorithm to facilitate the learning process
  4. None of these
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The additional acquaintance used by a learning algorithm to facilitate the learning process

14. Which of the following is true for Classification?

  1. subdivision of a set
  2. A measure of the accuracy
  3. The task of assigning a classification
  4. All of these

subdivision of a set

Data mining and Warehousing mcq

15. Data mining is?

  1. time variant non-volatile collection of data
  2. The actual discovery phase of a knowledge
  3. The stage of selecting the right data
  4. None of these
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The actual discovery phase of a knowledge

16. ——- is not a data mining functionality?

A) Clustering and Analysis
B) Selection and interpretation
C) Classification and regression
D) Characterization and Discrimination

Selection and interpretation

17. Which of the following can also applied to other forms?

a) Data streams & Sequence data
b) Networked data
c) Text & Spatial data
d) All of these

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All of these

18. ——– is the out put of KDD

a) Query
b) Useful Information
c) Data
d) information

Useful Information

19. What is noise?

a) component of a network
b) context of KDD and data mining
c) aspects of a data warehouse
d) None of these

context of KDD and data mining

data mining and warehousing mcq sppu

20. Firms that are engaged in sentiment mining are analyzing data collected from?

A. social media sites.
B. in-depth interviews.
C. focus groups.
D. experiments.

social media sites.

21. Which of the following forms of data mining assigns records to one of a
predefined set of classes?

(A). Classification
(B). Clustering
(C). Both A and B
(D). None

Clustering

22. The learning which is used to find the hidden pattern in unlabeled data is called?

(A). Unsupervised learning
(B). Supervised learning
(C). Reinforcement learning

Unsupervised learning

23. The learning which is the example of Self-organizing maps?

(A). Reinforcement learning
(B). Supervised learning
(C). Unsupervised learning
(D). Missing data imputation

Unsupervised learning

24. According to storks’ population size, find the total number of babies from the
following example of predicting the number of babies.

(A). feature
(B). outcome
(C). attribute
(D). observation

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outcome

25. Which of the following is not belong to data mining?

(A). Knowledge extraction
(B). Data transformation
(C). Data exploration
(D). Data archaeology

Data archaeology

26. The learning which is used for inferring a model from labeled training data is
called?

(A). Unsupervised learning
(B). Reinforcement learning
(C). Supervised learning
(D). Missing data imputation

Supervised learning

27. Which of the following is the right approach to Data Mining?

(A). Infrastructure, exploration, analysis, exploitation, interpretation
(B). Infrastructure, exploration, analysis, interpretation, exploitation
(C). Infrastructure, analysis, exploration, interpretation, exploitation
(D). None of these

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Infrastructure, exploration, analysis, interpretation, exploitation

28. Which of the following terms is used as a synonym for data mining?

(A). knowledge discovery in databases
(B). data warehousing
(C). regression analysis
(D). parallel processing in databases

knowledge discovery in databases

29. …………………..is an essential process where intelligent methods are applied to
extract data patterns

A) Data Warehousing
B) Data Mining
C) Data Base
D) Data Structure

Data Mining

30. Data mining requires

  1. Large quantities of operational data stored over a period of time
  2. Lots of tactical data
  3. Several tape drives to store archival data
  4. Large mainframe computers

Large quantities of operational data stored over a period of time

data mining and warehousing mcq questions

31. Data by itself is not useful unless

  1. It is massive
  2. It is processed to obtain information
  3. It is collected as a raw data from diverse sources
  4. It is properly stated

It is processed to obtain information

32. Which of the following is NOT example of ordinal attributes?

  1. Zip codes
  2. Ordered numbers
  3. Ascending or descending names
  4. Military ranks

Zip codes

33. In asymmetric attribute

  1. Order of values is important
  2. All values are equals
  3. Only non-zero value is important
  4. Range of values is important

Only non-zero value is important

34. Identify the example of Nominal attribute

  1. Temperature
  2. Mass
  3. Salary
  4. Gender
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Gender

35. Which of the following is not a data pre-processing methods?

  1. Data Visualization
  2. Data Discretization
  3. Data Cleaning
  4. Data Reduction

Data Visualization

36. Correlation analysis is used for __

  1. Handling missing values
  2. Identifying redundant attributes
  3. Handling different data formats
  4. Eliminating noise

Identifying redundant attributes

37. ______combines data from multiple sources into a coherent store

  1. Data Characterization
  2. Data Classification
  3. Data Integration
  4. Data Selection

Data Integration

38. Which of the following is / are attribute subset selection criterion(s) ?

  1. Forward selection
  2. Backward elimination
  3. Decision tree induction
  4. All of the above

All of the above

39. Data mining can also applied to other forms such as…………….

i) Data streams
ii) Sequence data
iii) Networked data
iv) Text data
v) Spatial data
A)i, ii, iii and v only
B) ii, iii, iv and v only
C) i, iii, iv and v only
D) All i, ii, iii, iv and v

All i, ii, iii, iv and v

40. ____ normalization is not very well efficient in handling the outliers
Min max

  1. Min max
  2. Z Score
  3. Decimal Scaling
  4. None of the above
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Min max

Data mining and warehousing mcq with answers

41. The full form of KDD is………………

A) Knowledge Database
B) Knowledge Discovery Database
C) Knowledge Data House
D) Knowledge Data Definition

Knowledge Discovery Database

Data Analytics sppu mcq

42. A collection of interesting and useful patterns in database is called ___

A. knowledge.
B. information.
C. data.
D. algorithm

knowledge.

43. Data ………………. is the process of finding a model that describes and
distinguishes data classes or concepts.

a)Characterization
b)Mining
c) clustering
d )Classification

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Classification

44. To remove noise and inconsistent data __ is needed

  1. Data Transformation
  2. Data Reduction
  3. Data Integration
  4. Data Cleaning

Data Cleaning

45.The terms equality and roll up are associated with _

  1. OLTP
  2. Visualization
  3. Data mart
  4. Decision Tree

Data mart

46. An operational system is which of the following?

A. A system that is used to run the business in real time and is based on historical data.
B. A system that is used to run the business in real time and is based on current
data.
C. A system that is used to support decision making and is based on current data.
D. A system that is used to support decision making and is based on historical data.

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A system that is used to run the business in real time and is based on current data.

47. Data warehouse is which of the following?

A. Can be updated by end users.
B. Contains numerous naming conventions and formats.
C. Organized around important subject areas.
D. Contains only current data.

Organized around important subject areas.

48. Data transformation includes which of the following?

A. A process to change data from a detailed level to a summary level
B. A process to change data from a summary level to a detailed level
C. Joining data from one source into various sources of data
D. Separating data from one source into various sources of data

A process to change data from a detailed level to a summary level

49. The ……………… allows the selection of the relevant information necessary for
the data warehouse.

A top-down view
B data warehouse view
C data source view
D business query view

A top-down view

50. Which of the following is not a component of a data warehouse?

A Metadata
B Current detail data
C Lightly summarized data
D Component Key

Component Key

51. Which of the following is not a kind of data warehouse application?

A Information processing
B Analytical processing
C Data mining
D Transaction processing

Transaction processing

52. ___ is not associated with data cleaning process.

  1. Deduplication
  2. Domain consistency
  3. Segmentation
  4. Disambiguation

Segmentation

53. Dimensionality refers to

  1. Cardinality of key values in a star schema
  2. The data that describes the transactions in the fact table
  3. The level of detail of data that is held in the fact table
  4. The level of detail of data that is held in the dimension table

The data that describes the transactions in the fact table

54. Expansion for DSS in DW is

  1. Decisive Strategic System
  2. Data Support System
  3. Data Store System
  4. Decision Support system

Decision Support system

55. Data in a data warehouse

  1. in a flat file format
  2. can be normalised but often is not
  3. must be in normalised form to at least 3NF
  4. must be in normalised form to at least 2NF

can be normalised but often is not

56. Friendship structure of users in a social networking site can be considered as
an example of ____

  1. Record data
  2. Ordered data
  3. Graph data
  4. None of the above

Graph data

57. A café owner wanted to compare how much revenue he gained from lattes
across different months of the year. What type of variable is ‘month’?

  1. Continuous
  2. Categorical
  3. Discrete
  4. Nominal

Categorical

58. An outlier is a _

  1. Description of records in the data
  2. Data point which is considered different from other data points
  3. Record with missing attributes
  4. Duplicate record

Data point which is considered different from other data points

59. Which of the following operations can be performed on ordinal attributes?

  1. Distictness
  2. Documents
  3. Both of the above
  4. None of the above

Both of the above

60. Height of a person, can be considered as an attribute of _____type?

  1. Nominal
  2. Ordinal
  3. Interval
  4. Ratio

Ratio

61. The cosine similarity measure counts for _

  1. The Euclidian distance between vectors
  2. The Manhattan distance between vectors
  3. The similarity of documents
  4. The dissimilarity of vectors
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The similarity of documents

62. Formula for dissimilarity computation between two objects for categorical
variable is – here p is categorical variable and m denotes number of matches

  1. D ( i, j ) = p – m / p
  2. D ( i, j ) = p – m / m
  3. D ( i, j ) = m – p / p
  4. D ( i, j ) = m – p / m

D ( i, j ) = p – m / p

63. Euclidean and Manhattan distances between the objects P, Q and R (1, 2, 3) and
(2, 1, 0) are _

  1. 3.32, 4 respectively
  2. 3.32, 5 respectively
  3. 5, 3.32 respectively
  4. 3.30, 3 respectively

3.32, 5 respectively

64. The main organisational justification for implementing a data warehouse is to
provide

  1. ETL from operation systems to strategic systems
  2. Large scale transaction processing
  3. Storing large volumes of data
  4. Decision support
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Decision support

65. A data warehouse

a. must import data from transactional systems whenever significant changes occur in the
transactional data
b. works on live transactional data to provide up to date and valid results
c. takes regular copies of transaction data
d. takes preprocessed transaction data and stores in a way that is optimised for
analysis

takes preprocessed transaction data and stores in a way that is optimised for analysis

66. Data warehouse contains ________data that is seldom found in the operational environment

  1. informational
  2. normalized
  3. denormalized
  4. summary

summary

67. In a snowflake schema which of the following types of tables is considered?

  1. Fact
  2. Dimension
  3. Both (a) and (b)
  4. None of the above
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Both (a) and (b)

68. Which of the following statements about data warehouse is true?

  1. A data warehouse is necessary to all those organisations that are using relational OLTP
  2. A data warehouse is useful to all organisations that currently use OLTP
  3. A data warehouse is valuable to the organisations that need to keep an audit trail of their activities
  4. A data warehouse is valuable only if the organisation has an interest in analysing historical data

A data warehouse is valuable only if the organisation has an interest in analysing historical data

69. When you ____ the data, you are aggregating the data to a higher level

  1. Slice
  2. Roll Up
  3. Roll Down
  4. Drill Down

Roll Up

70. The process of viewing the cross-tab (Single dimensional) with a fixed value of
one attribute is _

  1. Slicing
  2. Dicing
  3. Pivoting
  4. Both Slicing and Dicing
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Slicing

71. What do data warehouses support?

  1. OLAP
  2. OLTP
  3. OLAP and OLTP
  4. Operational databases

OLAP

72. A data cube consist of _

  1. Dimensional data
  2. Multidimensional data
  3. No dimensional data
  4. 1 dimensional data

Multidimensional data

73. Which type of data storage architecture gives fastest performance?

  1. ROLAP
  2. MOLAP
  3. HOLAP
  4. DOLAP
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MOLAP

74. Dissimilarity can be defined as __

  1. How much certain objects differ from each other
  2. How much certain objects simillar from each other
  3. Dissimilarities are non negative numbers d(i,j) that are small when i and j are close to each other and that become large when i and j are very different
  4. Both (a) and (c)

Both (a) and (c)

75. ______supports basic OLAP operations, including slice and dice, drill-down,
roll-up and pivoting

  1. Information processing
  2. Analytical processing
  3. Data processing
  4. Transaction processing

Analytical processing

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