MCQs

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Overall MCQs for Reference

1. Which algorithm is commonly used for classification tasks in machine learning?
   a. K-Means
   b. Linear Regression
   c. SVM
   d. PCA
   Answer: c. SVMsanfoundry​
2. Which of the following is a type of unsupervised learning?
   a. Linear Regression
   b. Logistic Regression
   c. K-Means Clustering
   d. Decision Tree
   Answer: c. K-Means Clusteringsanfoundry​
3. Which model is a generative model in machine learning?
   a. SVM
   b. Logistic Regression
   c. Naïve Bayes
   d. Linear Regression
   Answer: c. Naïve Bayessanfoundry​
4. Which algorithm outputs numerical values and is commonly used for regression?
   a. Decision Tree
   b. Naïve Bayes
   c. Linear Regression
   d. Logistic Regression
   Answer: c. Linear Regressionsanfoundry​
5. What is the main goal of regularization in machine learning models?
   a. Increase model complexity
   b. Reduce overfitting
   c. Speed up training
   d. Increase variance
   Answer: b. Reduce overfittinggeeksforgeeks​
6. Which of the following can only be used for linearly separable data?
   a. Hard-margin SVM
   b. Logistic Regression
   c. Soft-margin SVM
   d. Decision Tree
   Answer: a. Hard-margin SVMscribd​
7. Which of the following is a dimensionality reduction technique?
   a. Decision Tree
   b. PCA
   c. Random Forest
   d. Logistic Regression
   Answer: b. PCAaimcqs​
8. What is the primary advantage of using the Random Forest algorithm?
   a. Single decision path
   b. Higher interpretability
   c. Ensemble averaging reduces overfitting
   d. Only works with categorical variables
   Answer: c. Ensemble averaging reduces overfittingaimcqs​
9. What does the bias-variance tradeoff refer to?
   a. Training time issues
   b. Model complexity vs. generalization ability
   c. Number of trees vs. depth
   d. Precision vs. recall
   Answer: b. Model complexity vs. generalization abilityaimcqs​
10. Which function is commonly used as an activation function in the output layer for binary classification in neural networks?
    a. Tanh
    b. Sigmoid
    c. Softmax
    d. ReLU
    Answer: b. Sigmoidscribd​
11. What technique is used to randomly deactivate neurons during training?
    a. Max pooling
    b. Batch normalization
    c. Dropout
    d. L1 regularization
    Answer: c. Dropoutscribd​
12. Which machine learning model works well for text classification and uses conditional independence assumptions?
    a. Decision Tree
    b. K-Nearest Neighbors
    c. Naïve Bayes
    d. AdaBoost
    Answer: c. Naïve Bayesdiptisurvase.wordpress​
13. Which is a boosting algorithm in ensemble methods?
    a. Bagging
    b. Random Forest
    c. AdaBoost
    d. K-Means
    Answer: c. AdaBoostgyansetu​
14. Which algorithm is used for hierarchical clustering?
    a. K-Means
    b. Agglomerative clustering
    c. Decision Tree
    d. Naïve Bayes
    Answer: b. Agglomerative clusteringaimcqs​
15. Which method can be used for handling missing data?
    a. Data augmentation
    b. Imputation
    c. Bagging
    d. Boosting
    Answer: b. Imputationgyansetu​
16. Which kernel is most commonly used in SVM to handle non-linear data?
    a. Linear
    b. Polynomial
    c. Gaussian (RBF)
    d. Sigmoid
    Answer: c. Gaussian (RBF)sanfoundry​
17. What is the main idea behind ensemble learning?
    a. Use one model with higher complexity
    b. Combine predictions of multiple models
    c. Reduce the number of parameters
    d. Only use unsupervised models
    Answer: b. Combine predictions of multiple modelssanfoundry​
18. In which of the following models is early stopping used as a regularization technique?
    a. SVM
    b. Decision Tree
    c. Neural Networks
    d. Naïve Bayes
    Answer: c. Neural Networksscribd​
19. Which of the following is NOT a supervised learning algorithm?
    a. Decision Tree
    b. K-Nearest Neighbors
    c. K-Means Clustering
    d. Random Forest
    Answer: c. K-Means Clusteringinterviewbit​
20. Which of the following is used for time series forecasting?
    a. DBSCAN
    b. ARIMA
    c. SVM
    d. Naïve Bayes
    Answer: b. ARIMAinterviewbit​
21. Which cost function is suited for regression problems?
    a. Cross-entropy loss
    b. Mean squared error
    c. Hinge loss
    d. Log loss
    Answer: b. Mean squared errorsanfoundry​
22. What type of learning uses rewards and punishments to train models?
    a. Supervised
    b. Unsupervised
    c. Reinforcement
    d. Deep
    Answer: c. Reinforcementinterviewbit​
23. Which scaling technique transforms features to a 0–1 range?
    a. Z-score scaling
    b. Min-Max scaling
    c. Standardization
    d. Normalization
    Answer: b. Min-Max scalinggyansetu​
24. What is transfer learning primarily used for?
    a. Gathering more data
    b. Adapting pre-trained models to new tasks
    c. Hyperparameter tuning
    d. Feature selection
    Answer: b. Adapting pre-trained models to new tasksgyansetu​
25. Which technique is commonly used to tune hyperparameters?
    a. Feature extraction
    b. Cross-validation
    c. One-hot encoding
    d. Clustering
    Answer: b. Cross-validationinterviewbit​
26. Which of these algorithms is most sensitive to outliers?
    a. Decision Tree
    b. K-Means
    c. Logistic Regression
    d. Naïve Bayes
    Answer: b. K-Meanssanfoundry​
27. In which scenario would the softmax activation function be used?
    a. Binary classification
    b. Multi-class classification
    c. Regression
    d. Clustering
    Answer: b. Multi-class classificationscribd​
28. What is the purpose of feature engineering?
    a. Increase overfitting
    b. Enhance model performance
    c. Reduce number of samples
    d. Remove labels
    Answer: b. Enhance model performancescribd​
29. Which neural network architecture is most effective for sequential data?
    a. CNN
    b. RNN
    c. SVM
    d. Random Forest
    Answer: b. RNNscribd​
30. What is ‘bagging’ in ensemble methods?
    a. Building models sequentially
    b. Building models independently in parallel
    c. Reducing features
    d. Adding regularization
    Answer: b. Building models independently in parallelsanfoundry​
31. In machine learning, what is an epoch?
    a. A single input data point
    b. One complete pass through the training set
    c. The test data
    d. A model parameter
    Answer: b. One complete pass through the training setgyansetu​
32. What is a hyperparameter in machine learning?
    a. A parameter learned during training
    b. A setting specified before training begins
    c. A data label
    d. A model output
    Answer: b. A setting specified before training beginsinterviewkickstart​
33. Which is NOT a popular optimization method for neural networks?
    a. Gradient descent
    b. Newton’s method
    c. L-BFGS
    d. K-Means clustering
    Answer: d. K-Means clusteringsanfoundry​
34. What does PCA minimize in data?
    a. Maximum variance
    b. Mean squared error
    c. Covariance
    d. Overfitting
    Answer: a. Maximum varianceaimcqs​
35. Which technique can help prevent vanishing gradient problems in deep neural networks?
    a. ReLU activation
    b. Sigmoid activation
    c. Dropout
    d. Early stopping
    Answer: a. ReLU activationscribd​
36. Which algorithm is best suited for market basket analysis?
    a. Logistic Regression
    b. Apriori
    c. SVM
    d. K-Means
    Answer: b. Aprioriaimcqs​
37. Which ensemble method builds models sequentially and each focuses on previous errors?
    a. Random Forest
    b. Bagging
    c. Boosting
    d. K-Means
    Answer: c. Boostinggyansetu​
38. Which is the main purpose of L1 regularization?
    a. Add penalty to large weights, encourage sparsity
    b. Penalize small weights only
    c. Only for classification
    d. For unsupervised models
    Answer: a. Add penalty to large weights, encourage sparsitygeeksforgeeks​
39. Which algorithm can be used for anomaly detection?
    a. Logistic Regression
    b. K-Means
    c. LSTM
    d. ARIMA
    Answer: b. K-Meanssanfoundry​
40. What does the ROC curve illustrate?
    a. Bias-variance tradeoff
    b. Model complexity
    c. Tradeoff between true positive rate and false positive rate
    d. Regularization effects
    Answer: c. Tradeoff between true positive rate and false positive ratesanfoundry​
41. Which is a common optimizer in deep learning frameworks?
    a. AdaGrad
    b. Gradient boosting
    c. Apriori
    d. Decision Tree
    Answer: a. AdaGradinterviewkickstart​
42. What is the main application of convolutional neural networks (CNNs)?
    a. Text analysis
    b. Image processing
    c. Clustering
    d. Reinforcement learning
    Answer: b. Image processingscribd​
43. Which algorithm models output probabilities for classification using a logistic function?
    a. SVM
    b. Logistic Regression
    c. K-Means
    d. Random Forest
    Answer: b. Logistic Regressionsanfoundry​
44. Which type of machine learning involves a sequence of actions in an environment?
    a. Supervised learning
    b. Semi-supervised learning
    c. Reinforcement learning
    d. Deep learning
    Answer: c. Reinforcement learninginterviewbit​
45. What is the purpose of the attention mechanism in deep learning models?
    a. Increase overfitting
    b. Focus on relevant input parts
    c. Reduce model size
    d. Regularization
    Answer: b. Focus on relevant input partsdigitaldefynd​
46. Which dimension reduction is commonly used in visualization of high-dimension data?
    a. K-means
    b. Decision trees
    c. PCA
    d. SVM
    Answer: c. PCAaimcqs​
47. What is batch normalization used for?
    a. Prevent overfitting
    b. Speed up training and stabilize learning
    c. Increase model complexity
    d. Reduce feature size
    Answer: b. Speed up training and stabilize learningscribd​
48. Which learning method is best for problems with sequential data dependencies?
    a. CNNs
    b. RNNs
    c. SVMs
    d. KNNs
    Answer: b. RNNsscribd​
49. What does L2 regularization mainly penalize?
    a. Large weights (by adding squared weight penalty)
    b. Small weights
    c. Only biases
    d. Only input nodes
    Answer: a. Large weights (by adding squared weight penalty)geeksforgeeks​
50. What is the function of an embedding layer in deep learning?
    a. Calculate loss
    b. Transform categorical variables into dense vectors
    c. Pool features
    d. Reduce gradient vanishing
    Answer: b. Transform categorical variables into dense vectorsscribd​

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12. https://www.igmguru.com/blog/machine-learning-mcqs
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14. https://www.youtube.com/watch?v=FugpqOEPuBg
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17. https://www.placementpreparation.io/mcq/machine-learning/
18. https://inttrvu.ai/top-25-machine-learning-interview-questions-and-answer/
19. https://www.sanfoundry.com/types-of-machine-learning-supervised-unsupervised-reinforcement/
20. https://www.multisoftsystems.com/interview-questions/ai-and-machine-learning-interview-questions-answers