50 most important Data Warehousing mcq

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Q. 1. For Apriori algorithm, what is the second phase?

A : Pruning
B : Partitioning
C : Candidate generation
D : Itemset generation

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Pruning


Q. 3. Which of the following is not a type of constraints?

A : Data constraints
B : Rule constraints
C : Knowledge type constraints
D : Time constraints

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Time constraints


Q. 5. If two documents are similar, then what is the measure of angle between two documents?

A : 30
B : 60
C : 90
D : 0

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0 (when angle is 0 value of cosine is 1)

Q. 8. The fact is also called as

A : Dimension
B : Key
C : Schema
D : Measure

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Measure


Q. 9. The most widely used metrics and tools to assess a classification model are:

A : Conusion Matrix
B : Support
C : Entropy
D : Probability

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Conusion Matrix

Q. 10. A person trained to interact with a human expert in order to capture their knowledge.

A : knowledge programmer
B : knowledge developer
C : knowledge engineer
D : knowledge extractor

knowledge engineer


Q. 11. Training process that generates tree is called as

A : Pruning
B : Rule generation
C : Induction
D : spliiting

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Induction


Q. 12. The schema is collection of stars. Recognize the type of schema.

A : Star Schema
B : Snowflake schema
C : Fact constellation
D : Database schema

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Fact constellation



Q. 14. to evaluate a classifier’s quality we use

A : confusion matrix
B : error detection code
C : error correction code
D : classifier

confusion matrix


Q. 15. For Apriori algorithm, what is the first phase?

A : Pruning
B : Partitioning
C : Candidate generation
D : Itemset generation

Candidate generation


Q. 16. The example of knowledge type constraints in constraint based mining is

A : Association or Correlation
B : Rule templates
C : Task relevant data
D : Threshold measures

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Association or Correlation


Q. 18. A data cube is defined by

A : Dimensions
B : Facts
C : Dimensions and Facts
D : Dimensions or Facts

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Dimensions and Facts


Q. 19. Which one of the following is true for decision tree

A : Decision tree is useful in decision making
B : Decision tree is similar to OLTP
C : Decision Tree is similar to cluster analysis
D : Decision tree needs to find probabilities of hypothesis

Decision tree is useful in decision making


Q. 20. What are two steps of tree pruning work?

A : Pessimistic pruning and Optimistic pruning
B : Postpruning and Prepruning
C : Cost complexity pruning and time complexity pruning
D : None of the options

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Postpruning and Prepruning


Q. 21. The Microsoft SQL Server 2000 is the example of

A : ROLAP
B : MOLAP
C : HOLAP
D : HaoLap

HOLAP


Q. 22. The property of Apriori algorithm is

A : All nonempty subsets of a frequent itemsets must also be frequent
B : All empty subsets of a frequent itemsets must also be frequent
C : All nonempty subsets of a frequent itemsets must be not frequent
D : All nonempty subsets of a frequent itemsets can frequent or not frequent

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All nonempty subsets of a frequent itemsets must also be frequent


Q. 23. Multilevel association rule mining is

A : Association rules generated from candidate-generation method
B : Association rules generated from without candidate-generation method
C : Association rules generated from mining data at multiple abstarction level
D : Assocation rules generated from frequent itemsets

Association rules generated from mining data at multiple abstarction level


Q. 24. Which of the following activities is a data mining task?

A : Monitoring the heart rate of a patient for abnormalities
B : Extracting the frequencies of a sound wave
C : Predicting the outcomes of tossing a (fair) pair of dice
D : Dividing the customers of a company according to their profitability

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Monitoring the heart rate of a patient for abnormalities



Q. 26. Sensitivity is also referred to as

A : misclassification rate
B : true negative rate
C : True positive rate
D : correctness

True positive rate

Q. 27. In Apriori algorithm, for generating e. g. 5 itemsets, we use

A : Frequent 5 itemsets
B : Frequent 3 itemsets
C : Frequent 4 itemsets
D : Frequent 6 itemsets

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Frequent 4 itemsets

Q. 28. Handwritten digit recognition classifying an image of a handwritten
number into a digit from 0 to 9 is example of

A : Multiclassification
B : Multi-label classification
C : Imbalanced classification
D : Binary Classification

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Multiclassification

Q. 29. A lattice of cuboids is called as

A : Data cube
B : Dimesnion lattice
C : Master lattice
D : Fact table

Data cube

Q. 30. Specificity is also referred to as

A : true negative rate
B : correctness
C : misclassification rate
D : True positive rate

true negative rate

Q. 31. To improve the accuracy of multiclass classification we can use

A : cross validation
B : sampling
C : Error-detecting codes
D : Error-correcting codes

Error-correcting codes

Q. 33. The Galaxy Schema is also called as

A : Star Schema
B : Snowflake schema
C : Fact constellation
D : Database schema

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Lift ratioFact constellation

Q. 34. For a classification problem with highly imbalanced class. The majority class is observed 99% of times in the training data. Your model has 99% accuracy after taking the predictions on test data. Which of the following is not true in such a case?

A : Imbalaced problems should not be measured using Accuracy metric.
B : Accuracy metric is not a good idea for imbalanced class problems.
C : Precision and recall metrics aren’t good for imbalanced class problems.
D : Precision and recall metrics are good for imbalanced class problems.

Precision and recall metrics aren’t good for imbalanced class problems.

Q. 35. one-versus-one(OVO) and one-versus-all (OVA) classification involves

A : more than two classes
B : Only two classes
C : Only one class
D : No class

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more than two classes

Q. 36. How are metarules useful in mining of association rules?

A : Allow users to specify threshold measures
B : Allow users to specify task relevant data
C : Allow users to specify the syntactic forms of rules
D : Allow users to specify correlation or association

Allow users to specify the syntactic forms of rules

Q. 37. OLAP Summarization means

A : Consolidated
B : Primitive
C : Highly detailed
D : Recent data

Consolidated

Q. 38. A frequent pattern tree is a tree structure consisting of

A : A frequent-item-node
B : An item-prefix-tree
C : A frequent-item-header table
D : both B and C

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both B and C

Q. 40. Cross validation involves

A : testing the machine on all possible ways by substituting the original sample into
training set
B : testing the machine on all possible ways by dividing the original sample into
training and validation sets.
C : testing the machine with only validation sets
D : testing the machine on only testing datasets.

testing the machine with only validation sets

Q. 41. Which one of these is a tree based learner?

A : Rule based
B : Bayesian Belief Network
C : Bayesian classifier
D : Random Forest

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Random Forest

Q. 42. Ordinal attribute has three distinct values such as Fair, Good, and Excellent. If x and y are two objects of ordinal attribute with Fair and Good values respectively, then what is the distance from object y to x?


A : 1
B : 0
C : 0.5
D : 0.75

0.5

Q. 43. Rotating the axes in a 3-D cube is the examplele of

A : Pivot
B : Roll up
C : Drill down
D : Slice

Pivot

Q. 46. The tables are easy to maintain and saves storage space.

A : Star Schema
B : Snowflake schema
C : Fact constellation
D : Database schema

Snowflake schema

Q. 47. Accuracy is

A : Number of correct predictions out of total no. of predictions
B : Number of incorrect predictions out of total no. of predictions
C : Number of predictions out of total no. of predictions
D : Total number of predictions

Number of correct predictions out of total no. of predictions



Q. 48. What is the range of the angle between two term frequency vectors?

A : Zero to Thirty
B : Zero to Ninety
C : Zero to One Eighty
D : Zero to Fourty Five

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Zero to Ninety



Q. 50. Transforming a 3-D cube into a series of 2-D planes is the examplele of

A : Pivot
B : Roll up
C : Drill down
D : Slice

Slice

Q. 51. A model makes predictions and predicts 120 examples as belonging to the
minority class, 90 of which are correct, and 30 of which are incorrect. Precision of
model is

A : Precision = 0.89
B : Precision = 0.23
C : Precision = 0.45
D : Precision = 0.75

Precision = 0.75

Q. 53. A data normalization technique for real-valued attributes that divides
each numerical value by the same power of 10.

A : min-max normalization
B : z-score normalization
C : decimal scaling
D : decimal smoothing

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decimal scaling

Q. 55. In Binning, we first sort data and partition into (equal-frequency) bins
and then which of the following is not valid step

A : smooth by bin boundaries
B : smooth by bin median
C : smooth by bin means
D : smooth by bin values

smooth by bin values

Q. 58. precision of model is 0.75 and recall is 0.43 then F-Score is

A : F-Score= 0.99
B : F-Score= 0.84
C : F-Score= 0.55
D : F-Score= 0.49

F-Score= 0.55

Q. 59. The basic idea of the apriori algorithm is to generate the item sets of a
particular size & scans the database. These item sets are

A : Primary
B : Secondary
C : Superkey
D : Candidate

Candidate

Q. 60. How the bayesian network can be used to answer any query?

A : Full distribution
B : Joint distribution
C : Partial distribution
D : All of the mentioned

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Joint distribution

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