Q. 1. For Apriori algorithm, what is the second phase?
A : Pruning
B : Partitioning
C : Candidate generation
D : Itemset generation
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
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
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
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
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
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
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
Association or Correlation
Q. 18. A data cube is defined by
A : Dimensions
B : Facts
C : Dimensions and Facts
D : Dimensions or Facts
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
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
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
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
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
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
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
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
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
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
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
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
Joint distribution
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