MCQs

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Overall MCQs for Reference

1. Which regularization technique penalizes the absolute value of model coefficients in linear regression?
   a. L1 Regularization
   b. L2 Regularization
   c. Dropout
   d. Batch Normalization
   Answer: a. L1 Regularizationigmguru​
2. Which ensemble algorithm builds trees that correct errors of prior trees?
   a. Random Forest
   b. AdaBoost
   c. Bagging
   d. Ridge Regression
   Answer: b. AdaBoostgyansetu​
3. The kernel trick is primarily associated with which algorithm?
   a. Decision Trees
   b. Naive Bayes
   c. Support Vector Machines
   d. K-Means Clustering
   Answer: c. Support Vector Machinessanfoundry​
4. What is the purpose of the validation set in machine learning?
   a. To train the model
   b. To fine-tune hyperparameters
   c. To test the model’s generalization
   d. All of the above
   Answer: b. To fine-tune hyperparametersaimcqs​
5. Which method prevents overfitting by randomly dropping units during training?
   a. Early Stopping
   b. Bagging
   c. Dropout
   d. Regularization
   Answer: c. Dropoutigmguru​
6. The ROC curve is useful for evaluating which type of models?
   a. Regression
   b. Clustering
   c. Classification
   d. Dimensionality Reduction
   Answer: c. Classificationgyansetu​
7. Principal Component Analysis (PCA) is mainly used for:
   a. Classification
   b. Clustering
   c. Feature Extraction
   d. Bagging
   Answer: c. Feature Extractionscribd​
8. Which technique helps overcome the vanishing gradient problem in deep networks?
   a. Data Augmentation
   b. Batch Normalization
   c. Dropout
   d. Weight Decay
   Answer: b. Batch Normalizationigmguru​
9. Which algorithm is suitable for non-linear decision boundaries?
   a. Logistic Regression
   b. Linear Regression
   c. SVM with RBF Kernel
   d. Naive Bayes
   Answer: c. SVM with RBF Kernelgyansetu​
10. Which approach combines weak learners to make a strong learner?
    a. Pruning
    b. Bagging
    c. Boosting
    d. Feature Selection
    Answer: c. Boostinggyansetu​
11. Which loss function is typically used for regression tasks?
    a. Cross-Entropy Loss
    b. Hinge Loss
    c. Mean Squared Error
    d. Log Loss
    Answer: c. Mean Squared Errorplacementpreparation​
12. What is Gradient Boosting best known for?
    a. Reducing variance
    b. Reducing bias
    c. Increasing overfitting
    d. Linear separation
    Answer: b. Reducing biasgyansetu​
13. Which metric is suitable for imbalanced classification datasets?
    a. Accuracy
    b. F1-score
    c. RMSE
    d. Sensitivity
    Answer: b. F1-scoreaimcqs​
14. In t-SNE, what is the main goal?
    a. Classification
    b. Regression
    c. Non-linear dimensionality reduction
    d. Pruning
    Answer: c. Non-linear dimensionality reductionigmguru​
15. What does “K” in KNN refer to?
    a. Number of classes
    b. Number of features
    c. Number of neighbors
    d. Kernel used
    Answer: c. Number of neighborsgyansetu​
16. What does the term “bagging” stand for?
    a. Bayesian Aggregation
    b. Bootstrap Aggregating
    c. Batch Gradient
    d. Backpropagation Algorithm
    Answer: b. Bootstrap Aggregatinggyansetu​
17. Which deep learning architecture is best for image processing?
    a. Recurrent Neural Networks
    b. Transformers
    c. Convolutional Neural Networks
    d. Random Forests
    Answer: c. Convolutional Neural Networksigmguru​
18. What is backpropagation mainly used for?
    a. Feature selection
    b. Hyperparameter tuning
    c. Training neural networks
    d. Dimensionality reduction
    Answer: c. Training neural networkssanfoundry​
19. What is a common approach for hyperparameter optimization?
    a. Grid Search
    b. SVM
    c. Data Imputation
    d. Bagging
    Answer: a. Grid Searchstudocu​
20. In unsupervised learning, which algorithm is used for grouping similar data?
    a. Decision Trees
    b. K-Means Clustering
    c. AdaBoost
    d. Ridge Regression
    Answer: b. K-Means Clusteringsanfoundry​
21. LSTM networks are designed to handle what problem in sequence modeling?
    a. Overfitting
    b. Vanishing gradient
    c. Class imbalance
    d. Linear separation
    Answer: b. Vanishing gradientigmguru​
22. What does “curse of dimensionality” refer to?
    a. Overfitting in high data volume
    b. Increased computational complexity with many features
    c. Lack of interpretability
    d. Decreased accuracy with more data
    Answer: b. Increased computational complexity with many featuresigmguru​
23. Which activation function can suffer from dying neuron problem?
    a. Sigmoid
    b. Tanh
    c. ReLU
    d. Softmax
    Answer: c. ReLUgyansetu​
24. What is the objective of dropout regularization?
    a. Reduce training speed
    b. Improve data augmentation
    c. Prevent overfitting
    d. Increase model complexity
    Answer: c. Prevent overfittingigmguru​
25. Which technique is useful for handling missing data?
    a. Normalization
    b. Imputation
    c. Regularization
    d. Bagging
    Answer: b. Imputationstudocu​
26. Which method estimates confidence intervals for prediction uncertainty?
    a. Ensemble Averaging
    b. Bayesian Inference
    c. Principal Component Analysis
    d. Early Stopping
    Answer: b. Bayesian Inferencelinkedin​
27. What is transfer learning?
    a. Learning from the same dataset
    b. Applying learned knowledge to a new but similar task
    c. Linear modeling of data
    d. Recurrent network training
    Answer: b. Applying learned knowledge to a new but similar tasklinkedin​
28. In reinforcement learning, what is a “policy”?
    a. Reward function
    b. Sequence of states
    c. Mapping from states to actions
    d. Discount factor
    Answer: c. Mapping from states to actionslinkedin​
29. Which algorithm is NOT considered an ensemble method?
    a. Bagging
    b. Boosting
    c. Dropout
    d. Random Forest
    Answer: c. Dropoutgyansetu​
30. What is vanishing gradient problem associated with?
    a. KNN
    b. Deep Neural Networks
    c. Random Forest
    d. Logistic Regression
    Answer: b. Deep Neural Networksigmguru​
31. Which is NOT a method for scaling features?
    a. Min-Max Scaling
    b. Standardization
    c. Data Augmentation
    d. Robust Scaling
    Answer: c. Data Augmentationigmguru​
32. What is the output of a softmax activation function?
    a. Binary value
    b. Probability distribution
    c. Integer value
    d. Error rate
    Answer: b. Probability distributiongyansetu​
33. What problem does Random Forest address compared to a single Decision Tree?
    a. High bias
    b. Overfitting
    c. Linear separation
    d. Pruning complexity
    Answer: b. Overfittinggyansetu​
34. Dropout can be applied during:
    a. Training only
    b. Testing only
    c. Both training and testing
    d. Neither stage
    Answer: a. Training onlyigmguru​
35. What does the loss function measure in supervised learning?
    a. Number of features
    b. Model accuracy
    c. Difference between predicted and actual output
    d. Data size
    Answer: c. Difference between predicted and actual outputplacementpreparation​
36. Which algorithm assumes feature independence?
    a. Naive Bayes
    b. Decision Trees
    c. Random Forest
    d. SVM
    Answer: a. Naive Bayesgyansetu​
37. Why is feature selection important in high-dimensional data?
    a. To increase overfitting
    b. Reduce computational complexity
    c. Add noise
    d. Increase the number of features
    Answer: b. Reduce computational complexityigmguru​
38. Which technique allows models to generalize better on unseen data?
    a. Overfitting
    b. Early Stopping
    c. Increasing training epochs
    d. None of the above
    Answer: b. Early Stoppingigmguru​
39. What is a “Hyperparameter” in machine learning?
    a. Output from model
    b. Parameter that shapes learning process
    c. Direct input data
    d. Objective function
    Answer: b. Parameter that shapes learning processgyansetu​
40. What is the main difference between Bagging and Boosting?
    a. Boosting uses sequential models, bagging uses parallel models
    b. Bagging increases bias, boosting increases variance
    c. Both are exactly same
    d. None of the above
    Answer: a. Boosting uses sequential models, bagging uses parallel modelslinkedin​
41. What is the advantage of using Ensemble Models?
    a. Reduced computational cost
    b. Improved accuracy and robustness
    c. Increased model bias
    d. Simplified model architecture
    Answer: b. Improved accuracy and robustnessgyansetu​
42. Which method randomly selects data samples to train each basic model in an ensemble?
    a. Boosting
    b. Bagging
    c. Dropout
    d. Gradient Descent
    Answer: b. Bagginggyansetu​
43. Why would a model use one-hot encoding for categorical variables?
    a. To reduce overfitting
    b. To make the data more interpretable
    c. To allow algorithms to use binary input
    d. To compress data
    Answer: c. To allow algorithms to use binary inputplacementpreparation​
44. Which advanced optimization can be used for training neural nets more efficiently?
    a. Adam
    b. Logistic Regression
    c. Naive Bayes
    d. KNN
    Answer: a. Adamigmguru​
45. In clustering, what metric is commonly used for evaluating the effectiveness of clusters?
    a. R-squared
    b. RMSE
    c. Silhouette Score
    d. F1-Score
    Answer: c. Silhouette Scoreigmguru​
46. What technique is often used with decision trees to avoid overfitting?
    a. Pruning
    b. Early Stopping
    c. Feature Extraction
    d. Imputation
    Answer: a. Pruningsanfoundry​
47. What is the role of the activation function in neural networks?
    a. Increase learning rate
    b. Control regularization
    c. Introduce non-linearity
    d. Reduce computational complexity
    Answer: c. Introduce non-linearitysanfoundry​
48. Which technique is not used for dimensionality reduction?
    a. PCA
    b. t-SNE
    c. K-Means
    d. LDA
    Answer: c. K-Meansigmguru​
49. What are Convolutional Neural Networks best suited for?
    a. Sequential Data
    b. Tabular Data
    c. Image Data
    d. Text Data
    Answer: c. Image Dataigmguru​
50. Which of the following is NOT a boosting algorithm?
    a. AdaBoost
    b. Gradient Boosting
    c. Random Forest
    d. XGBoost
    Answer: c. Random Forestgyansetu​

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2. https://www.gyansetu.in/blog/machine-learning-mcq-interview-preparation/
3. https://www.sanfoundry.com/1000-machine-learning-questions-answers/
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13. https://www.youtube.com/watch?v=1iRydu3quZ8
14. https://www.scribd.com/document/892178398/20cs20906-advanced-Machine-Learning-question-Bank
15. https://github.com/fatosmorina/machine-learning-exams
16. https://www.youtube.com/watch?v=FugpqOEPuBg
17. https://practicetestgeeks.com/machine-learning-practice-test-exam/
18. https://www.studeersnel.nl/nl/document/vrije-universiteit-amsterdam/advanced-machine-learning/2021-exam-solutions/60218545