100 Data Mining and Data Warehousing MCQs in Pdf

100 Data mining and data warehousing multiple choice questions with answers Pdf for the preparation of academic and competitive IT exams.

Preparing for an exam can be a challenging task, especially when it comes to subjects like data mining and data warehousing. With the ever-increasing importance of these fields in today’s data-driven world, it is crucial to have a strong grasp on the fundamental concepts and principles.

However, finding reliable resources to practice and test your knowledge can often be a daunting process. If you are currently in search of a comprehensive set of 100 multiple-choice questions specifically tailored to evaluate your understanding in data mining and data warehousing, look no further – you have landed in the right place.

Definitions of Data Mining:

Data Mining Definition 1: Data mining refers to the process of discovering valuable patterns, relationships, and insights within large datasets using advanced techniques such as machine learning, statistics, and artificial intelligence.

Data Mining Definition 2: Data mining is the systematic exploration of vast data repositories to extract hidden knowledge, enabling organizations to make informed decisions and gain competitive advantages.

Data Mining Definition 3: Data mining involves the automated discovery of meaningful patterns, trends, and anomalies from complex data sources, enhancing the understanding of underlying relationships.

Data Mining Definition 4: Data mining is the art of sifting through data to unveil concealed patterns and trends, providing actionable insights that drive strategic decision-making.

Data Mining Definition 5: Data mining is the science of extracting valuable information from diverse datasets, enabling organizations to uncover novel insights and predict future outcomes.

How is data mining application?

Data mining is used to analyze customer behavior, optimize supply chains, detect credit card fraud, predict disease outbreaks, and improve manufacturing processes, among other applications.

Data Warehousing Meaning:

Data warehousing entails creating a unified and organized storage environment for data. It ensures that data is readily accessible for analysis, reporting, and other business intelligence activities.

What is the Data Warehouse Definition?

A data warehouse is a structured storage system that stores historical and transactional data from multiple sources. It supports decision-making by providing a reliable and consolidated view of organizational data.

What Is a Data Warehouse?

A data warehouse is a central repository that stores data from various sources, facilitating data analysis, reporting, and decision-making.

100 Data Mining and Data Warehousing MCQs in Pdf

1. A priori algorithm operates in ___ method
a. Bottom-up search method
b. Breadth-first search method
c. None of above
d. Both a & b

2. A bi-directional search takes advantage of ___ process
a. Bottom-up process
b. Top-down process
c. None
d. Both a & b

3. The pincer-search has an advantage over a priori algorithm when the largest frequent item set is long.
a. True
b. false

4. MCFS stand for
a. Maximum Frequent Candidate Set
b. Minimal Frequent Candidate Set
c. None of above

5. MFCS helps in pruning the candidate set
a. True
b. False

6. DIC algorithm stands for ___
a. Dynamic itemset counting algorithm
b. Dynamic itself counting algorithm
c. Dynamic item set countless algorithms
d. None of above

7. If the item set is in a dashed circle while completing a full pass it moves towards
a. Dashed circle
b. Dashed box
c. Solid Box
d. Solid circle

8. If the item set is in the dashed box then it moves into a solid box after completing a full pass
a. True
b. False

9. The dashed arrow indicates the movement of the item set
a. True
b. False

10. The vertical arrow indicates the movement of the item set after reaching the frequency threshold
a. True
b. False

11. Frequent set properties are:
a. Downward closure property
b. Upward closure property
c. A & B
d. None of these

12. Any subset of a frequent set is a frequent set is
A. Downward closure property
B. Upward closure property
C. A and b

13. Periodic maintenance of a data mart means
a. Loading
b. Refreshing
c. Purging
d. All are true

14. The Fp-tree Growth algorithm was proposed by
a. Srikant
b. Aggrawal
c. Hanetal
d. None of these

15. The main idea of the algorithm is to maintain a frequent pattern tree of the date set. An extended prefix tree structure starting crucial and quantitative information about frequent sets
a. Priori Algorithm
b. Pinchers Algorithm
c. FP- Tree Growth algo.
d. All of these

16. The data warehousing and data mining technologies have extensive potential applications in the govt in various central govt sectors such as :
a. Agriculture
b. Rural Development
c. Health and Energy
d. all of the true

17. ODS Stands for
a. External operational data sources
b. operational data source
c. output data source
d. none of the above

18. Good performance can be achieved in a data mart environment by extensive use of
a. Indexes
b. creating profile records
c. volumes of data
d. all of the above

19. Features of Fp tree are
(i). It is dependent on the support threshold
(ii). It depends on the ordering of the items
(iii). It depends on the different values of trees
(iv). It depends on frequent itemsets with respect to give information
a. (i) & (ii)
b. (iii) & (iv)
c. (i) & (iii)
d. (ii) only

20. For a list T, we denote head_t as its first element and body-t as the remaining part of the list (the portion of the list T often removal of head_t) thus t is
a. {head} {body}
b. {head_t} {body_t}
c. {t_head}{t_body}
d. None of these

21. Partition Algorithm executes in
a. One phase
b. Two-Phase
c. Three phase
d. None of these

22. In the First Phase of the Partition Algorithm
a. Logically divides into a number of non-overlapping partitions
b. Logically divides into a number of overlapping Partitions
c. Not divides into partitions
d. Divides into non-logically and non-overlapping Partitions

23. Functions of the second phase of the partition algorithm are
a. Actual support of item sets are generated
b. Frequent itemsets are identified
c. Both (a) & (b)
d. None of these

24. Partition algorithm is based on the
a. Size of the global Candidate set
b. Size of the local Candidate set
c. Size of frequent itemsets
d. No. Of item sets

25. Pincer search algorithm based on the principle of
a. Bottom-up
b. Top-Down
c. Directional
d. Bi-Directional

26. Pincer-Search Method Algorithm contains
(i) Frequent item set in a bottom-up manner
(ii) Recovery procedure to recover candidates
(iii) List of maximal frequent itemsets
(iv) Generate a number of partitions
a. (i) only
b. (i) & (iii) only
c. (i),(iii) & (iv)
d. (i),(ii)&(iii)

27. Is a full-breadth search, where no background knowledge of frequent itemsets is used for pruning?
a. Level-crises filtering by the single item
b. Level-by-level independent
c. Multi-level mining with uniform support
d. Multi-level mining with reduced support

28. Disadvantage of uniform support is
a. Items at lower levels of abstraction will occur as frequently.
b. If the minimum support threshold is set too high, I could miss several meaningful associations
c. Both (a) & (b)
d. None of these

29.Warehouse administrator responsible for
a. Administrator
b. maintenance
c. both a and b
d. none of the above

30. The pincer-search has an advantage over a priori algorithm when the largest frequent itemset is long
a. True
b. false

31. What are the common approaches to tree pruning?
a. Prepruning and Postpruning approach.
b. Prepruning.
c. Postpruning.
d. None of the above.

32. Tree pruning methods address this problem of ___?
a. Overfitting the branches
b. Overfitting the data
c. a and b both
d. None of the above

33. What is the Full Form of MDL.
a. Maximum Description Length
b. Minimum Description Length
c. Mean Described Length
d. Minimum Described Length

34. State that the Statements are True / False:
a. Post pruning approach Removes Branches from a ‘Fully Grown’ Tree.
a. True
b. False

b. The “Best Pruned Tree is the one that maximizes the number of encoding bits.
a. True
b. False

35. Upon halting, the node becomes a ___
A. Heap
B. Subset
C. Leaf
D. Superset

36. demographic and neural clustering are methods of clustering based on
a. data types
b. methodology of calculation
c. Inter record distance
d. all of the above

37. POS stands for
a. Peer of sale
b. Point of sale
c. part of the sale
d. none of the above

38. Classification and Prediction are two forms of
a. Data analysis
b. Decision Tree
c. A and B
d. None of these

39. Classification predicts
a. Categorical labels
b. Prediction models continued valued function
c. A and B
d. None of these

40. True / False
a. Each Tuple is assumed to belong to a predefined class as determined by one of the attributes, called the class label attribute.
b. The individual tuples making up the training set are referred to as the training data set.
c. Classification and Regression are the two major type of data analysis.
Ans. A-True, B-True, C-False

41.True / False
a. Classification and Regression are the two major type of data analysis.
b. Classification is used to predict discrete or nominal values.
c. Regression is used to predict continuous or ordered values.
d. All are true

42. Classification and Prediction have numerous applications:
a. Credit approval
b. Medical diagnosis
c. Performance prediction & selective marketing
d. All of these

43. Class label of each training sample is provided with this step is known as
a. Unsupervised learning
b. Supervised learning
c. Training samples
d. Clustering

44. Decision tree is based on
a. Bottom-down technique
b. Top-down technique
c. Divide-and-conquer manner
d. Top-down recursive divide-and-conquer manner

45. Recursive Partitioning stops in Decision Tree when
a. All samples for a given node belong to the same class.
b. There are no remaining attributes on which samples may be further partitioned.
c. There are no samples for the branch test.
d. All the above.

46. To select the test attribute of each node in a decision tree we use
a. Entity Selection Measure
b. Data Selection Measure
c. Information Gain Measure
d. None of these

47.Test attribute for the current node in the decision tree is chosen on the basis of
a. Lowest entity gain
b. Highest data gain
c. Highest Information Gain
d. Lowest Attribute Gain

48. Advantage of the Information-theoretic approach of the decision tree is
a. Minimizes the expected number of tests needed
b. Minimizes the number of Nodes
c. Maximizes the number of nodes
d. Maximizes the number of tests

49. Let us be the no. of samples of S in class Ci then expected information to classify a given sample is given by
a. L(s1,s2,……..sm)=_log2(pi)
b. L(s1,s2,……..sm)=-_pilog2(pi)
c. L(s1,s2,……..sm)=_pilog2x
d. L(s1,s2,……..sm)=_pilog2(pi)

50. Steps applied to the data in order to improve the accuracy, efficiency, and scalability are:-
a. Data cleaning
b. Relevance analysis
c. Data transformation
d. All of the above

51. The process used to remove or reduce noise and the treatment of missing values
a. Data cleaning
b. Relevance analysis
c. Data transformation
d. None of above

52. Relevance analysis may be performed on the data by removing any irrelevant attribute from the process.
a. True
b. False

53. Classification and prediction method can be affected by:-
a. Accuracy & Speed
b. Robustness & Scalability
c. Interpretability
d. All of the above

54. In a decision tree internal node denotes a test on an attribute and Leaf nodes represent classes or class distributions
a. True
b. false

55. ___ attempts to identify and remove branches, with Improving accuracy
a. decision tree
b. tree pruning
c. both of them
d. none of above

56. To deal with larger data sets, a sampling method, called ___
a. Clara
b. Dara
c. Pam
d. None

57. What is the Full Form of CLARA.
a. Clustering Large Applicant
b. Close Large Applicant
c. Clustering Large Applications
d. None of the above

58. What is the Full Form of CLARANS.
a. Clustering Large Applications Based Upon Randomized Search
b. Close Large Applicant Based Upon Role Search
c. Clustering Large Applicant Based Upon Randomized Search
d. None of the above

59. Which Algorithm was proposed that combines the Sapling Technique with PAM.
a. CLARA
b. CLARANS
c. Both a and b
d. None of these.

60. Which are the two type of Hierarchical Clustering?
a. Agglomerative Hierarchical Clustering and Density Hierarchical Clustering
b. Agglomerative Hierarchical Clustering and Divisive Hierarchical Clustering
c. Divisive Hierarchical Clustering and Density Hierarchical Clustering
d. None of the above

61. Cluster is a :
a. The process of grouping a set of physical or abstract objects into classes of similar objects is called clustering.
b. A cluster of data objects can be treated collectively as one group in many applications
c. Cluster analysis is an important human activity.
d. All of the above

62. Cluster analysis tools based on
a. K-means
b. K-medosis
c. A and B
d. None of these

63. S-Plus, SPSS, SAS software packages use for
a. Data Mining
b. Classification
c. Clustering
d. Prediction

64. Unsupervised learning is an example of
a. Classification and prediction
b. Classification and Regression
c. clustering
d. Data Mining

65. Requirement of Clustering in Data Mining
a. Scalability
b. Ability to deal with different types of attributes
c. Ability to deal with noisy data
d. Discovery of clusters with arbitrary shape
e. Minimal requirement for domain knowledge to determine input parameters
f. Insensitivity to the order of input records
g. High dimensionality
h. Constraint-based clustering
(a). a, c, d, f
(b). g, h
(c). All of these
(d.) None of these

66. Clustering method can be classified
a. Partitioning Methods
b. Hierarchical methods
c. Density-based methods
d. All of these

67. Hierarchical methods can be classified
a. Agglomerative Approach
b. Divisive Approach
c. A and B
d. None of these

68. Agglomerative approach is called as
a. Bottom-up Approach
b. Top-Down Approach
c. A and B
d. None of these

69. Top-Down Approach is
a. Agglomerative Approach
b. Divisive Approach

70. Drawback of Hierarchical Methods
a. Suffer from the fact that once a step is done, it can never be undone.
b. A technique is that they cannot correct erroneous decision.
c. Both a & b
d. None of these

71. Two approaches to improving the quality of hierarchical clustering:
a. Perform careful analysis of object “linkages” at each hierarchical partitioning, such as in CURE and Chameleon
b. Integrate Hierarchical agglomeration and iterative relocation by first using a hierarchical agglomerative algorithm and refining the result using an iterative relocation
c. Both a & b
d. None of these

72. Classical Portioning methods are
a. k-means and k-median
b. k-means and k-medoids
c. k-modes only
d. none of these

73. K-means technique is based on
a. Centroid Object
b. Reference object
c. Representative object
d. Partition Object

74. K-medoids technique is based on
a. Centroid Object
b. Representative object
c. Partition Object
d. None of these

75. The k-means and the k-modes methods can be integrated to cluster data with mixed numeric and categorical values, resulting in
a. k-median method
b. k-partition method
c. k-prototypes method
d. k-medoids method

76. The squared-error criterion is used in a k-means method defined as
a. E=_I=1tok _pεci [p-m_i]
b. E=_I=1tok _pεci [m_i]₂
c. E=_I=1tok _pεci [p]₂
d. E=_I=1tok _pεci [p-mi]₂

77. The Computational Complexity of the k-means method algorithm is
a. O(log x)
b. Θ(nkt)
c. O(nkt)
d. Θ(log x)

78. Which Method is more Robust-k-means or k-medoids?
a. The k means is more robust in the presence of noise
b. The k-medoids method is more robust in the presence of noise and outliers
c. The k-medoids method is more robust due to no. of partitions
d. The k means is more robust due to its less complexity

79. First k-medoids algorithm introduced is
a. Prototype Above Medoids
b. Partition Below Medoids
c. Prototype Around Medoids
d. Partitioning Around Medoids

80. PAM stands for
a. Prototype Above Medoids
b. Prototype Around Means
c. Partitioning Around Medoids
d. Partitioning Above Means

81.Which statements are true fork-means
(i). It can apply only when the mean of the cluster is defined.
(ii). It is not suitable for discovering clusters with non-convex shapes
(iii). This method is relatively efficient in processing only small data.
a. (i) only
b. (i) & (ii) only
c. (iii) only
d. All the above

82 DBSCAN stands for:
a. Divisive Based Clustering Method
b. Density-Based Clustering Method
c. Both a & b
d. None of above

83: DBSCAN defines a cluster as a maximal set of density –
Connected points
a. True
b. False

84: For a non-negative value ε,N_e(O_i)={ O_j ∈D I d(O_i,O_j)≤ ε}
a. True
b. false

85. The ___ client is a desktop that relies on the server to which it is connected for the majority of its computing power.
a. thin
b. none
c. thick
d. web server

86. An object is said to be the Core Object if
a._ N_e(O)_ ≥ MinPts
b. _ N (O)_ ≥ MaxPts
c. none of above
d. both a & b

87. The density-reachability relation is transitive but not symmetric
a. True
b. False

88. Non-core objects are:-
a. border object
b. noise object
c. non-object
d. both a & b

89. DBSCAN algorithm can classify into:
a. classified
b. unclassified
c. noise
d. all of above

90. Unsupervised learning is an example of
a. Classification and prediction
b. Classification and Regression
c. clustering
d. Data Mining

91. Data can be classified as
a. reference data
b. transaction data and derived data
c. derived data
d. all of the above

92. Reference and transaction data originates from
a. operational system
b. Unnormalized data
c. data marts
d. all are true

93. Derived data is derived from
a. reference data
b. transaction data
c. reference and transaction data
d. none of the above

94. Unnormalized data, which is the basis for online analytical processing tools are prepared periodically but is directly based on detailed ___.
a. reference data
b. transaction data
c. reference and transaction data
d. none of the above

95. The data mart is loaded with data from a data warehouse by means of a ___
a. load program
b. process
c. project
d. all is valid

96. The chief considerations for a Load program are:
a. frequency and schedule
b. total or partial refreshment
c. customization and re-sequencing
d. all are true

97. Periodic maintenance of a data mart means
a. all are true
b. loading
c. refreshing
d. purging

98. Detailed level data, summary level, preprocessed and Adhoc data are data in
a. data warehouse
b. data mart
c. both
d. none of the above

99. Data sources in the data warehouse are referred to as
a. External data source
b. Operational data source
c. External operational data source
d. none of the above

100. ___ Table help and enable the end-users of the data mart to relate the data to its expanded version.
a. data
b. reference
c. both a and b
d. none of the above

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