Practice Library
All MCQs
Browse exam-wise, subject-wise, and country-wise MCQs with explanations.
Choose an option to check your answer.
A.
An unavailable or unrecorded attribute value
B.
A value equal to zero in every case
C.
A duplicated row
D.
A value outside one standard deviation
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Correct Answer: A. An unavailable or unrecorded attribute value
Explanation:
Missingness means the true value is not present in the dataset.
It should not automatically be interpreted as zero.
Choose an option to check your answer.
A.
A numeric variable with a large range
B.
A balanced binary target
C.
A table sorted by date
D.
The same country recorded as 'Pakistan', 'PK', and 'Pak'
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Correct Answer: D. The same country recorded as 'Pakistan', 'PK', and 'Pak'
Explanation:
Inconsistent coding represents the same concept in multiple forms.
Standardization is needed so equivalent values are treated as one category.
Choose an option to check your answer.
A.
To increase every numeric value
B.
To convert all variables to text
C.
To detect and correct errors, inconsistencies, and missing values
D.
To guarantee a balanced class distribution
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Correct Answer: C. To detect and correct errors, inconsistencies, and missing values
Explanation:
Data cleaning improves reliability before modeling.
Poor-quality inputs can create misleading patterns and unstable models.
Choose an option to check your answer.
A.
A change in file format only
B.
A change over time in the relationship between predictors and the target
C.
A reduction in storage capacity
D.
A random change in class names
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Correct Answer: B. A change over time in the relationship between predictors and the target
Explanation:
When the predictive relationship changes, an old model may no longer be valid.
Examples include evolving fraud strategies or customer behavior.
Choose an option to check your answer.
A.
Data distributions and relationships may change over time
B.
The training data become larger automatically
C.
Model parameters cannot be saved
D.
Monitoring increases the number of labels
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Correct Answer: A. Data distributions and relationships may change over time
Explanation:
Concept drift and data drift can reduce model performance after deployment.
Monitoring helps detect degradation and trigger retraining or review.
Choose an option to check your answer.
A.
Training the first candidate model
B.
Removing all outliers
C.
Drawing a summary histogram
D.
Integrating a validated model into an operational process
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Correct Answer: D. Integrating a validated model into an operational process
Explanation:
Deployment makes model outputs available for real decisions or automated actions.
It also requires monitoring, maintenance, and governance.
Choose an option to check your answer.
A.
It guarantees zero model error
B.
It replaces preprocessing
C.
It helps define meaningful problems and interpret discovered patterns
D.
It removes the need for validation
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Correct Answer: C. It helps define meaningful problems and interpret discovered patterns
Explanation:
Domain knowledge guides feature selection, constraints, and practical interpretation.
Without context, an algorithm may find patterns that are statistically real but irrelevant.
Choose an option to check your answer.
A.
Business usefulness
B.
Support
C.
Novelty to a manager
D.
Ease of explanation to a specific audience
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Correct Answer: B. Support
Explanation:
Support is computed directly from observed frequencies.
Usefulness and novelty depend partly on stakeholder goals and prior knowledge.
Choose an option to check your answer.
A.
A pattern that is valid, novel, useful, and understandable
B.
Any pattern with many variables
C.
A rule with the longest text
D.
A result produced by the most complex algorithm
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Correct Answer: A. A pattern that is valid, novel, useful, and understandable
Explanation:
Interestingness combines statistical strength with practical relevance.
A frequent pattern may still be unhelpful if it is obvious or unactionable.
Choose an option to check your answer.
A.
A guarantee that more features improve accuracy
B.
A database storage error
C.
A rule that all features must be binary
D.
The deterioration of distance, density, and sample coverage as feature count grows
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Correct Answer: D. The deterioration of distance, density, and sample coverage as feature count grows
Explanation:
In high dimensions, data become sparse and distances often become less informative.
Algorithms may require much more data and careful feature selection.
Choose an option to check your answer.
A.
The number of class labels only
B.
The number of algorithms tested
C.
The number of attributes or features
D.
The physical file size
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Correct Answer: C. The number of attributes or features
Explanation:
Dimensionality counts the variables used to describe each observation.
High-dimensional data can create computational and statistical challenges.
Choose an option to check your answer.
A.
A one-dimensional histogram
B.
A graph of nodes and edges
C.
A simple ordered list only
D.
A single regression coefficient
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Correct Answer: B. A graph of nodes and edges
Explanation:
Social actors are represented as nodes and relationships as edges.
Graph structure enables analysis of communities, influence, and connectivity.