MCQs

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Overall MCQs for Reference

1. Which term describes the process of transforming raw data into informative inputs for machine learning models?
   a. Feature Engineering
   b. Model Tuning
   c. Cost Analysis
   d. Algorithm Optimization
   Answer: a. Feature Engineeringh2o+1​
2. What is the main goal of feature engineering?
   a. Increase training data size
   b. Improve model performance
   c. Reduce code size
   d. Ignore irrelevant data
   Answer: b. Improve model performancemlexam+1​
3. Which is a common technique for handling missing values?
   a. One-hot encoding
   b. Imputation
   c. Clustering
   d. Tokenization
   Answer: b. Imputationmilvus​
4. Which method transforms categorical data into multiple binary columns?
   a. Scaling
   b. One-hot encoding
   c. Binning
   d. Standardization
   Answer: b. One-hot encodingjoinmytutor+1​
5. Which feature engineering technique converts an ordinal category into integers?
   a. Binning
   b. Normalization
   c. Label encoding
   d. PCA
   Answer: c. Label encodinggeeksforgeeks​
6. What is the goal of feature selection?
   a. Add random noise
   b. Choose most relevant features
   c. Split the dataset
   d. Lower the algorithm’s complexity only
   Answer: b. Choose most relevant featureseasyexamnotes+1​
7. Which reduces the range of features to ?cloudskillsboost​
   a. Min-max normalization
   b. Binning
   c. Imputation
   d. Principal Component Analysis
   Answer: a. Min-max normalizationmilvus​
8. What does PCA stand for in feature engineering?
   a. Principal Component Algorithm
   b. Principal Component Analysis
   c. Primary Categorical Algorithm
   d. Probabilistic Component Analysis
   Answer: b. Principal Component Analysismlexam+1​
9. Which is not a feature extraction technique?
   a. PCA
   b. SVD
   c. One-hot encoding
   d. t-SNE
   Answer: c. One-hot encodingeasyexamnotes+1​
10. Which term describes creating new variables from existing ones?
    a. Feature reduction
    b. Feature creation
    c. Outlier removal
    d. Feature pruning
    Answer: b. Feature creationh2o+1​
11. Which method adds random values to features to increase diversity?
    a. Regularization
    b. Data augmentation
    c. Feature scaling
    d. Stacking
    Answer: b. Data augmentationaionlinecourse​
12. Which approach sequentially adds features to improve performance?
    a. Sequential Forward Selection
    b. Backward Elimination
    c. Random Forest
    d. PCA
    Answer: a. Sequential Forward Selectioneasyexamnotes​
13. What is feature scaling used for?
    a. Increase interpretability
    b. Normalize features’ range
    c. Remove duplicates
    d. Select top features
    Answer: b. Normalize features’ rangegyansetu+1​
14. Which is not an encoding method for categorical variables?
    a. One-hot encoding
    b. Label encoding
    c. Binning
    d. Frequency encoding
    Answer: c. Binninggeeksforgeeks​
15. Creating a new feature by multiplying ‘age’ and ‘income’ is called:
    a. Feature corruption
    b. Feature interaction
    c. Feature pruning
    d. Feature normalization
    Answer: b. Feature interactionkdnuggets+1​
16. Which method is commonly used to reduce dimensionality?
    a. One-hot encoding
    b. PCA
    c. Data binning
    d. Mean imputation
    Answer: b. PCAmlexam+1​
17. Removing features with high correlation helps reduce:
    a. Overfitting
    b. Model accuracy
    c. Model interpretability
    d. Prediction speed
    Answer: a. Overfittingjoinmytutor​
18. Which algorithm iteratively removes less relevant features?
    a. Random Forest
    b. Backward Elimination
    c. SVM
    d. k-NN
    Answer: b. Backward Eliminationeasyexamnotes​
19. Filling missing values based on median is called:
    a. Random sampling
    b. Median imputation
    c. Feature extraction
    d. Feature aggregation
    Answer: b. Median imputationmilvus​
20. Which method is efficient for large datasets but may ignore feature interactions?
    a. Filter
    b. Wrapper
    c. Embedded
    d. Binning
    Answer: a. Filtercliffsnotes+1​
21. Which approach evaluates subsets of features using model performance?
    a. Filter
    b. Wrapper
    c. Embedded
    d. Sampling
    Answer: b. Wrappercliffsnotes​
22. What is feature importance in tree-based models?
    a. Weight assigned to features based on splits
    b. Random assignment
    c. Model bias
    d. Default scaling
    Answer: a. Weight assigned to features based on splitsdatacamp​
23. Converting continuous variable into categorical bins is called:
    a. Imputation
    b. Binning
    c. Normalization
    d. Label encoding
    Answer: b. Binningaionlinecourse+1​
24. What is outlier detection in feature engineering?
    a. Removing missing values
    b. Identifying and handling unusual values
    c. Encoding categories
    d. Splitting features
    Answer: b. Identifying and handling unusual valuesaionlinecourse​
25. What is variance threshold?
    a. Feature selection based on correlation
    b. Removing features with low variance
    c. Standard scaling
    d. Creating bins
    Answer: b. Removing features with low variancejoinmytutor​
26. Which type of feature selection uses statistical tests?
    a. Wrapper
    b. Filter
    c. Embedded
    d. Hybrid
    Answer: b. Filtercliffsnotes+1​
27. Which of the following is not a transformation method?
    a. Log transformation
    b. Scaling
    c. Data leakage
    d. Normalization
    Answer: c. Data leakagekdnuggets+1​
28. Grouping rare categories into “Other” is an example of:
    a. Feature binning
    b. Feature creation
    c. Regularization
    d. Standardization
    Answer: a. Feature binninggeeksforgeeks+1​
29. Creating a feature from the date, like “day of the week,” is called:
    a. Date encoding
    b. Feature extraction
    c. Feature pruning
    d. Label encoding
    Answer: b. Feature extractionkdnuggets+1​
30. What is the major advantage of dimensionality reduction?
    a. Increased overfitting
    b. Decreased interpretability
    c. Reduced computational cost
    d. Lose information
    Answer: c. Reduced computational costmlexam+1​
31. Why is standardization important?
    a. Makes models simpler
    b. Puts features on the same scale
    c. Reduces feature count
    d. Selects best features only
    Answer: b. Puts features on the same scalejoinmytutor+1​
32. Which technique improves model robustness by adding new variation to data?
    a. One-hot encoding
    b. Data augmentation
    c. Normalization
    d. t-SNE
    Answer: b. Data augmentationaionlinecourse​
33. What is the process of converting text to numbers known as?
    a. Tokenization
    b. Encoding
    c. Feature scaling
    d. Binning
    Answer: b. Encodinggeeksforgeeks+1​
34. Removing duplicate features is a type of:
    a. Feature extraction
    b. Feature cleaning
    c. Feature augmentation
    d. Regularization
    Answer: b. Feature cleaningmlexam​
35. Which method splits continuous data into intervals?
    a. Scaling
    b. Binning
    c. Clipping
    d. Imputation
    Answer: b. Binningaionlinecourse+1​
36. Combining several weak features to make a strong one is called:
    a. Feature aggregation
    b. Feature pruning
    c. Feature leakage
    d. Feature visualization
    Answer: a. Feature aggregationkdnuggets+1​
37. In tree-based models, what helps with variable importance analysis?
    a. Gini importance
    b. One-hot encoding
    c. Binning
    d. Label encoding
    Answer: a. Gini importancedatacamp​
38. Which is not a step in feature engineering?
    a. Feature selection
    b. Feature grouping
    c. Model ensembling
    d. Feature extraction
    Answer: c. Model ensemblingh2o+1​
39. What technique is used for splitting categorical columns into separate features?
    a. Feature splitting
    b. One-hot encoding
    c. Standardization
    d. Scaling
    Answer: b. One-hot encodingjoinmytutor+1​
40. What is regularization often used for in feature engineering?
    a. Adding features
    b. Penalizing complex models
    c. Encoding data
    d. Splitting data
    Answer: b. Penalizing complex modelsh2o​
41. What is a feature cross?
    a. Multiplying two variables to create interaction
    b. Removing correlated features
    c. Splitting data
    d. Normalization
    Answer: a. Multiplying two variables to create interactionmilvus+1​
42. Which method is used for encoding ordinal variables?
    a. One-hot encoding
    b. Label encoding
    c. Random sampling
    d. PCA
    Answer: b. Label encodinggeeksforgeeks​
43. The process of generating synthetic data points is part of:
    a. Data cleaning
    b. Data augmentation
    c. Feature extraction
    d. Feature binning
    Answer: b. Data augmentationaionlinecourse​
44. Feature hashing is mainly used for:
    a. Reducing dimensionality
    b. Encoding large categorical variables
    c. Normalizing data
    d. Imputation
    Answer: b. Encoding large categorical variablesgeeksforgeeks+1​
45. Which is an embedded method for feature selection?
    a. Decision tree importance
    b. Correlation threshold
    c. Mean imputation
    d. Binning
    Answer: a. Decision tree importancedatacamp​
46. Which algorithm does NOT perform feature selection natively?
    a. LASSO
    b. Random Forest
    c. K-Nearest Neighbors
    d. Gradient Boosting
    Answer: c. K-Nearest Neighborsdatacamp​
47. Which tool can be used for feature engineering in Python?
    a. pandas
    b. NumPy
    c. scikit-learn
    d. All of the above
    Answer: d. All of the abovedatacamp+1​
48. Which statistical method is used to assess correlation for feature selection?
    a. Pearson’s correlation
    b. Mean encoding
    c. Normalization
    d. Feature interaction
    Answer: a. Pearson’s correlationgyansetu​
49. Which technique splits datasets into train and test sets?
    a. Data splitting
    b. Binning
    c. Sampling
    d. Both a and c
    Answer: d. Both a and ckdnuggets​
50. Why is feature engineering critical in AI?
    a. It directly influences model accuracy and performace
    b. It reduces data storage
    c. It decreases computational time only
    d. It is not critical
    Answer: a. It directly influences model accuracy and performanceh2o+1​

1. https://h2o.ai/wiki/feature-engineering/
2. https://www.igmguru.com/blog/artificial-intelligence-mcqs
3. https://www.mlexam.com/feature-engineering/
4. https://milvus.io/ai-quick-reference/what-are-feature-engineering-techniques-and-how-do-they-apply-to-a-dataset
5. https://www.joinmytutor.com/mcq/feature-engineering.php
6. https://www.geeksforgeeks.org/machine-learning/what-is-feature-engineering/
7. https://easyexamnotes.com/feature-extraction-selection-concepts-and-algorithms-mcqs/
8. https://www.cloudskillsboost.google/course_templates/11/quizzes/437493?locale=uk
9. https://www.aionlinecourse.com/blog/10-features-engineering-techniques-for-machine-learning
10. https://www.gyansetu.in/blog/machine-learning-mcq-interview-preparation/
11. https://www.kdnuggets.com/2018/12/feature-engineering-explained.html
12. https://www.cliffsnotes.com/study-notes/23749838
13. https://www.datacamp.com/tutorial/feature-engineering
14. https://www.projectpro.io/article/8-feature-engineering-techniques-for-machine-learning/423
15. https://www.sanfoundry.com/feature-engineering-selection-techniques/
16. https://www.sanfoundry.com/artificial-intelligence-questions-answers/
17. https://examradar.com/fundamentals-of-artificial-intelligence-ai-and-machine-learning-ml-mcqs/
18. https://www.scribd.com/document/568024950/AIML-mcq
19. https://study.com/academy/practice/quiz-worksheet-feature-engineering-in-machine-learning.html
20. https://www.aionlinecourse.com/ai-quiz-questions/machine-learning/data-pre-processing