MCQs

Disclaimer:

Educational Use Only:

The website links provided are to be used strictly for educational and knowledge-gaining purposes only.

No Misuse:

Do not misuse the links or the information contained therein for any commercial, illegal, or unethical activities.

Respect for Websites and Businesses:

Respect the intellectual property and business interests of the websites and their owners. Do not attempt to alter, copy, or redistribute content without proper authorization.

Acknowledge Value of Knowledge:

Recognize and value the knowledge and resources shared by these websites. Use the information responsibly and ethically.

No Endorsement:

The provision of these links does not imply endorsement of the linked sites, their content, or their products and services. Users are responsible for evaluating the suitability and accuracy of the information.

User Responsibility:

By accessing these links, you agree to use the information at your own risk and to comply with all applicable laws and website terms of use.

Overall MCQs for Reference

1. Which of the following is NOT a data reduction strategy?
   a. Dimensionality reduction
   b. Numerosity reduction
   c. Data compression
   d. Data migration
   Answer: d. Data migrationsanfoundry​
2. The process of reducing the number of random variables or input features in a dataset is called:
   a. Data integration
   b. Dimensionality reduction
   c. Data mining
   d. Normalization
   Answer: b. Dimensionality reductiongeeksforgeeks​
3. Sampling in data reduction refers to:
   a. Selecting a representative subset of data
   b. Adding noise to data
   c. Combining all variables
   d. Eliminating missing values
   Answer: a. Selecting a representative subset of datat4tutorials​
4. Which of the following is a parametric numerosity reduction technique?
   a. Histogram
   b. Clustering
   c. Regression
   d. Sampling
   Answer: c. Regressionsanfoundry​
5. Data cube aggregation is an example of:
   a. Dimensionality reduction
   b. Numeric scaling
   c. Parametric reduction
   d. Non-parametric numerosity reduction
   Answer: d. Non-parametric numerosity reductionsanfoundry​
6. Dimensionality reduction is used to:
   a. Increase features
   b. Reduce features
   c. Remove all features
   d. Duplicate features
   Answer: b. Reduce featuresaimcqs​
7. Which technique can reconstruct original data without information loss?
   a. Irreversible compression
   b. Lossy compression
   c. Lossless compression
   d. Feature selection
   Answer: c. Lossless compressionsanfoundry​
8. Which of the following is a dimensionality reduction technique?
   a. Data balancing
   b. Principal Component Analysis (PCA)
   c. Data duplication
   d. Data normalization
   Answer: b. Principal Component Analysis (PCA)aimcqs​
9. Non-negative Matrix Factorization (NMF) in data reduction:
   a. Finds orthogonal features
   b. Decomposes a non-negative matrix into two non-negative matrices
   c. Performs clustering
   d. Generates synthetic data
   Answer: b. Decomposes a non-negative matrix into two non-negative matricesaimcqs​
10. The key benefit of data reduction for mining algorithms is:
    a. Increased storage requirements
    b. Improved speed and efficiency
    c. Greater data complexity
    d. Higher computational costs
    Answer: b. Improved speed and efficiencyt4tutorials​
11. Which method helps reduce the impact of outliers in data?
    a. Data deletion
    b. Robust scaling
    c. Feature duplication
    d. Data labeling
    Answer: b. Robust scalinginterviewkickstart​
12. What is feature selection?
    a. Adding new data
    b. Removing irrelevant or redundant features
    c. Normalizing all features
    d. Data encryption
    Answer: b. Removing irrelevant or redundant featurest4tutorials​
13. The primary goal of dimensionality reduction is to:
    a. Retain important information with fewer features
    b. Remove all labels
    c. Increase dimensionality
    d. Combine unrelated features
    Answer: a. Retain important information with fewer featuresgeeksforgeeks​
14. Out of the following, which helps in reducing computational cost?
    a. Data normalization
    b. Feature selection
    c. Increasing data points
    d. Data encryption
    Answer: b. Feature selectiont4tutorials​
15. Which is NOT a benefit of data reduction?
    a. Reduces overfitting
    b. Saves storage space
    c. Decreases data accuracy
    d. Increases algorithm speed
    Answer: c. Decreases data accuracysanfoundry​
16. Which data reduction technique is best for removing duplicate records?
    a. Data imputation
    b. Data deduplication
    c. Data normalization
    d. Data sampling
    Answer: b. Data deduplicationt4tutorials​
17. Data compression is primarily done to:
    a. Transform feature sets
    b. Reduce storage space and processing time
    c. Increase data redundancy
    d. Combine classes
    Answer: b. Reduce storage space and processing timet4tutorials​
18. Which dimensionality reduction method is best for visualizing high-dimensional data?
    a. PCA
    b. LDA
    c. t-SNE
    d. Factor Analysis
    Answer: c. t-SNEaimcqs​
19. Which approach is used to handle categorical variables and preserve associations?
    a. PCA
    b. Multiple Correspondence Analysis
    c. Factor Analysis
    d. k-Means
    Answer: b. Multiple Correspondence Analysisaimcqs​
20. The mathematical method that transforms features into orthogonal components based on variance is:
    a. t-SNE
    b. PCA
    c. LDA
    d. ICA
    Answer: b. PCAaimcqs​
21. The process of aggregating data to create new variables is known as:
    a. Data normalization
    b. Data summarization
    c. Data normalization
    d. Data deduction
    Answer: b. Data summarizationt4tutorials​
22. Which of the following involves compressing data for storage efficiency?
    a. Feature selection
    b. Data imputation
    c. Data compression
    d. Data merging
    Answer: c. Data compressiont4tutorials​
23. Manifold learning in dimensionality reduction primarily captures:
    a. Linear relationships
    b. Non-linear structure
    c. Outlier influence
    d. Missing value patterns
    Answer: b. Non-linear structurestudyguides​
24. Which of the following reduction methods minimize reconstruction error?
    a. PCA
    b. t-SNE
    c. Autoencoder
    d. Regression
    Answer: c. Autoencoderaimcqs​
25. Which dimensionality reduction technique is suitable for text mining?
    a. PCA
    b. LDA
    c. t-SNE
    d. Latent Semantic Analysis
    Answer: d. Latent Semantic Analysisaimcqs​
26. Data integration for reduction means:
    a. Combining multiple data sources
    b. Increasing feature space
    c. Data encryption
    d. Random sampling
    Answer: a. Combining multiple data sourcesgtu-mcq​
27. Which of these is a non-parametric numerosity reduction technique?
    a. Regression
    b. Histogram
    c. Feature encoding
    d. Feature generation
    Answer: b. Histogramsanfoundry​
28. Redundant features in a dataset can be removed through:
    a. Clustering
    b. Feature selection
    c. Data normalization
    d. Data translation
    Answer: b. Feature selectiont4tutorials​
29. Factor analysis is often used to:
    a. Normalize data
    b. Explore relationships between variables
    c. Merge datasets
    d. Add more features
    Answer: b. Explore relationships between variablesaimcqs​
30. Which method scales down the dataset size by choosing “representative” samples?
    a. Data sampling
    b. Feature selection
    c. Data labeling
    d. Outlier removal
    Answer: a. Data samplingt4tutorials​
31. Which is NOT a commonly used data reduction technique?
    a. Feature selection
    b. Outlier injection
    c. Principal Component Analysis
    d. Data compression
    Answer: b. Outlier injectiont4tutorials​
32. Data cube aggregation is mainly useful for:
    a. Summarization
    b. Normalization
    c. Data enrichment
    d. Outlier removal
    Answer: a. Summarizationsanfoundry​
33. Which data reduction strategy preserves as much variance as possible?
    a. Normalization
    b. PCA
    c. Feature labeling
    d. Data deduplication
    Answer: b. PCAaimcqs​
34. Which is a disadvantage of dimensionality reduction?
    a. Data visualizations
    b. Loss of information
    c. Decreasing processing speed
    d. Reducing complexity
    Answer: b. Loss of informationgeeksforgeeks​
35. Data pruning refers to:
    a. Removing unneeded data
    b. Combining features
    c. Sampling data
    d. Adding artificial data
    Answer: a. Removing unneeded datastudyguides​
36. Which algorithm creates new features by linearly combining original features?
    a. PCA
    b. Regression
    c. k-Means
    d. Naive Bayes
    Answer: a. PCAaimcqs​
37. In data reduction, which of the following is a subset of the original variables?
    a. Aggregated features
    b. Sampled features
    c. Selected features
    d. Normalized features
    Answer: c. Selected featurest4tutorials​
38. What is the primary advantage of reducing data dimensionality before model training?
    a. Reducing model complexity and overfitting
    b. Increasing training time
    c. Creating more missing values
    d. Lowering accuracy
    Answer: a. Reducing model complexity and overfittinggeeksforgeeks​
39. Which of the following is most suitable for large datasets with sparse features?
    a. PCA
    b. Latent Semantic Analysis
    c. k-Means
    d. Data deduplication
    Answer: b. Latent Semantic Analysisaimcqs​
40. Which method is computationally expensive and best for small to medium datasets?
    a. PCA
    b. LDA
    c. t-SNE
    d. Histogram
    Answer: c. t-SNEaimcqs​
41. In dimensionality reduction, “curse of dimensionality” refers to problems caused by:
    a. Too few features
    b. Too many features
    c. Incorrect scaling
    d. Missing data
    Answer: b. Too many featuresgeeksforgeeks​
42. Which of the following maintains local similarities in a dataset when reducing dimensions?
    a. PCA
    b. t-SNE
    c. Normalization
    d. Sampling
    Answer: b. t-SNEaimcqs​
43. When does over-reduction of dimensions hurt model performance?
    a. When irrelevant features removed
    b. When vital information is discarded
    c. When data normalized
    d. Never
    Answer: b. When vital information is discardedgeeksforgeeks​
44. Which is an unsupervised technique for data reduction?
    a. PCA
    b. Random Forest
    c. Logistic Regression
    d. Linear Regression
    Answer: a. PCAaimcqs​
45. The main aim of numerosity reduction is to:
    a. Provide more accurate results
    b. Reduce the volume of data
    c. Increase redundancy
    d. Duplicate features
    Answer: b. Reduce the volume of datasanfoundry​
46. Data binning contributes to reduction by:
    a. Smoothing data
    b. Adding features
    c. Sampling data
    d. Duplicating outliers
    Answer: a. Smoothing datasanfoundry​
47. Which is not a dimensionality reduction method?
    a. t-SNE
    b. PCA
    c. Data labeling
    d. LDA
    Answer: c. Data labelingaimcqs​
48. The main operation during data reduction preprocessing is:
    a. Removing redundant records
    b. Data encryption
    c. Data masking
    d. Adding null values
    Answer: a. Removing redundant recordst4tutorials​
49. Which one of these focuses on reducing data redundancy?
    a. Data deduplication
    b. Data augmentation
    c. Data splitting
    d. Data sampling
    Answer: a. Data deduplicationt4tutorials​
50. A key goal of all data reduction techniques in data science is to:
    a. Preserve analytical results while reducing data
    b. Increase dataset size for better accuracy
    c. Add noise to datasets
    d. Extend processing time
    Answer: a. Preserve analytical results while reducing datasanfoundry​

1. https://www.sanfoundry.com/data-mining-questions-answers-data-reduction/
2. https://www.geeksforgeeks.org/quizzes/dimensionality-reduction-in-machine-learning/
3. https://t4tutorials.com/data-reduction-mcqs/
4. https://aimcqs.com/dimensionality-reduction
5. https://interviewkickstart.com/blogs/articles/data-science-mcq-answers
6. https://www.sanfoundry.com/1000-data-mining-questions-answers/
7. https://studyguides.com/study-methods/multiple-choice/clz8xjtvw6ivn47xc19p7y32h
8. https://gtu-mcq.com/BE/Computer-Engineering/Semester-6/3160714/5457/MCQs?q=bDFvcmdllQA%3D
9. https://www.interviewbit.com/data-science-mcq/
10. https://testbook.com/objective-questions/mcq-on-data-reduction-techniques–649050c79d1db05def0e2915
11. https://www.placementpreparation.io/mcq/data-science/
12. https://wayground.com/admin/quiz/67af4c0f61eb0e9d0c791e6a/exploring-data-reduction-techniques
13. http://www.datasciencelovers.com/blog/important-questions-and-answers-for-dimensionality-reductions/
14. https://www.gyansetu.in/blog/data-science-objective-type-mcq-quiz-interview-preparation/
15. https://www.scribd.com/document/483160319/DIP-II-UNIT
16. https://www.scribd.com/document/856379688/Data-Science-100-MCQs
17. https://www.studocu.com/in/quiz/mcq-on-data-science-part-2/1650570
18. https://easyexamnotes.com/data-warehousing-mcq/
19. https://devinterview.io/blog/dimensionality-reduction-interview-questions
20. https://mcqmate.com/discussion/114899/in-data-reduction