Artificial Intelligence and Machine Learning MCQs January 8, 2026July 11, 2024 by u930973931_answers 50 min Score: 0 Attempted: 0/50 Subscribe 1. Which of the following is not a type of artificial intelligence? (A) Narrow AI (B) General AI (C) Applied AI (D) Super AI 2. What is the primary objective of supervised learning? (A) To classify data into predefined categories (B) To discover patterns in unlabeled data (C) To predict an output variable based on input data (D) To improve the performance of reinforcement learning algorithms 3. Which technique is used to reduce the dimensionality of data in machine learning? (A) K-nearest neighbors (KNN) (B) Linear Regression (C) Principal Component Analysis (PCA) (D) Naive Bayes 4. What does the term “overfitting” refer to in machine learning? (A) When a model performs well on training data but not on unseen test data (B) When a model performs consistently on both training and test data (C) When a model is too simple to capture the underlying patterns in the data (D) When a model is too complex and learns noise from the training data 5. Which algorithm is used for anomaly detection? (A) Isolation Forest (B) K-means clustering (C) Support Vector Machines (SVM) (D) Decision Trees 6. Which technique is suitable for handling non-linear decision boundaries? (A) Logistic Regression (B) Support Vector Machines (SVM) (C) Linear Discriminant Analysis (LDA) (D) Decision Trees 7. In reinforcement learning, what does an agent learn from the environment? (A) Labeled examples (B) Rewards or penalties (C) Historical data (D) Optimal hyperparameters 8. Which neural network architecture is typically used for image recognition tasks? (A) Recurrent Neural Network (RNN) (B) Autoencoder (C) Long Short-Term Memory (LSTM) (D) Convolutional Neural Network (CNN) 9. Which evaluation metric is commonly used for imbalanced datasets? (A) Accuracy (B) Precision-Recall AUC (C) Mean Squared Error (MSE) (D) F1-score 10. Which technique is used to preprocess text data in natural language processing? (A) Principal Component Analysis (PCA) (B) Stemming and Lemmatization (C) One-hot encoding (D) K-means clustering 11. What is the purpose of the bias term in neural networks? (A) To reduce variance in the model (B) To regularize the model (C) To shift the activation function (D) To adjust the output along with the weights 12. Which algorithm is suitable for both classification and regression tasks? (A) Gradient Descent (B) K-means clustering (C) Random Forest (D) K-nearest neighbors (KNN) 13. Which type of machine learning algorithm is most appropriate for predicting stock prices? (A) Reinforcement Learning (B) Unsupervised Learning (C) Supervised Learning (D) Semi-supervised Learning 14. What is the purpose of dropout in neural networks? (A) To prevent overfitting (B) To speed up convergence (C) To increase the model complexity (D) To reduce computational cost 15. Which technique is used for collaborative filtering in recommendation systems? (A) K-means clustering (B) Apriori algorithm (C) Singular Value Decomposition (SVD) (D) Gradient Boosting 16. Which of the following is not a step in the machine learning pipeline? (A) Data Preprocessing (B) Model Validation (C) Algorithm Tuning (D) Database Query Optimization 17. What does the term “ensemble learning” refer to? (A) Training multiple models with different hyperparameters (B) Learning from a large dataset in a distributed computing environment (C) Using deep learning models for prediction tasks (D) A method where multiple models are combined to improve performance 18. What does the activation function in a neural network do? (A) It determines the number of layers in the network (B) It introduces non-linearity into the network (C) It normalizes the input data (D) It adjusts the learning rate during training 19. Which algorithm is used for time series forecasting? (A) Decision Trees (B) Long Short-Term Memory (LSTM) (C) K-means clustering (D) Support Vector Machines (SVM) 20. In which scenario would unsupervised learning be most appropriate? (A) Spam email detection (B) Sentiment analysis of customer reviews (C) Predicting housing prices based on historical data (D) Customer segmentation based on purchasing behavior 21. Which technique is used for feature selection in machine learning? (A) Learning rate adjustment (B) Hyperparameter tuning (C) Recursive Feature Elimination (RFE) (D) Model validation 22. Which type of neural network architecture is used for sequence prediction tasks? (A) Convolutional Neural Network (CNN) (B) Recurrent Neural Network (RNN) (C) Autoencoder (D) Deep Belief Network (DBN) 23. Which method is used to handle missing data in a dataset? (A) Data augmentation (B) Data standardization (C) Data normalization (D) Data imputation 24. Which approach is used to reduce the variance of a machine learning model? (A) Dropout (B) L2 Regularization (C) L1 Regularization (D) Batch Normalization 25. What is the purpose of the “softmax” function in a neural network? (A) To convert predictions into probabilities (B) To normalize the input data (C) To regularize the model (D) To reduce the computational cost 26. Which technique is used to prevent gradient vanishing or exploding in deep neural networks? (A) Gradient Descent (B) Batch Normalization (C) Data Augmentation (D) Learning Rate Scheduling 27. What is the main advantage of using a Gaussian Naive Bayes classifier? (A) It works well with complex non-linear decision boundaries (B) It is computationally efficient and simple to implement (C) It can handle missing values in the dataset (D) It provides high accuracy in high-dimensional spaces 28. Which type of learning algorithm does not require labeled training data? (A) Supervised Learning (B) Semi-supervised Learning (C) Reinforcement Learning (D) Unsupervised Learning 29. Which technique is used for reducing the dimensionality of sparse data? (A) Singular Value Decomposition (SVD) (B) Principal Component Analysis (PCA) (C) Linear Discriminant Analysis (LDA) (D) Ridge Regression 30. In which scenario would you use a Recurrent Neural Network (RNN) rather than a Feedforward Neural Network (FNN)? (A) Image classification (B) Speech recognition (C) Handwritten digit recognition (D) Text translation 31. Which technique is used for model evaluation when dealing with imbalanced classes? (A) Root Mean Squared Error (RMSE) (B) Mean Absolute Error (MAE) (C) R-squared (R2) score (D) ROC AUC Curve 32. What is the primary challenge in training deep neural networks? (A) Overfitting (B) Underfitting (C) Gradient vanishing or exploding (D) High computational cost 33. Which method is used for hyperparameter optimization in machine learning? (A) Grid Search (B) K-means clustering (C) Bagging (D) Cross-validation 34. Which technique is used for data augmentation in computer vision tasks? (A) Principal Component Analysis (PCA) (B) Dropout (C) Image rotation and flipping (D) Batch Normalization 35. Which algorithm is used for clustering in unsupervised learning? (A) Linear Regression (B) K-means clustering (C) K-nearest neighbors (KNN) (D) Gradient Boosting 36. Which method is used to handle multicollinearity in linear regression? (A) Lasso Regression (B) Logistic Regression (C) Ridge Regression (D) Naive Bayes 37. Which technique is used to reduce the learning rate dynamically during training? (A) Batch Normalization (B) Early Stopping (C) Learning Rate Decay (D) Dropout 38. What is the primary objective of the “bagging” technique in machine learning? (A) To reduce bias in the model (B) To preprocess the input data (C) To speed up convergence (D) To reduce variance in the model 39. Which technique is used for anomaly detection in network security? (A) Apriori algorithm (B) Isolation Forest (C) Linear Discriminant Analysis (LDA) (D) AdaBoost 40. In which type of machine learning problem would you use the “one-hot encoding” technique? (A) Regression (B) Classification (C) Clustering (D) Reinforcement Learning 41. Which approach is used for sentiment analysis of text data? (A) Word2Vec (B) Hashing Vectorizer (C) Support Vector Machines (SVM) (D) Bag of Words (BoW) 42. What is the purpose of the “ReLU” activation function in a neural network? (A) To normalize the input data (B) To convert predictions into probabilities (C) To introduce non-linearity (D) To reduce the computational cost 43. Which technique is used for time series data forecasting with long-term dependencies? (A) Autoencoder (B) AdaBoost (C) Principal Component Analysis (PCA) (D) Long Short-Term Memory (LSTM) 44. What does the term “underfitting” refer to in machine learning? (A) When a model performs well on training data but not on unseen test data (B) When a model is too simple to capture the underlying patterns in the data (C) When a model is too complex and learns noise from the training data (D) When a model does not converge during training 45. Which algorithm is used for recommendation systems based on collaborative filtering? (A) K-means clustering (B) Naive Bayes (C) Decision Trees (D) Singular Value Decomposition (SVD) 46. Which technique is used to handle class imbalance in classification problems? (A) SMOTE (Synthetic Minority Over-sampling Technique) (B) Naive Bayes (C) Gradient Boosting (D) L1 Regularization 47. What is the purpose of the “momentum” term in gradient descent optimization algorithms? (A) To increase the learning rate dynamically (B) To prevent overfitting (C) To speed up convergence (D) To reduce the variance in the model 48. Which type of neural network architecture is used for unsupervised learning tasks such as dimensionality reduction? (A) Convolutional Neural Network (CNN) (B) Recurrent Neural Network (RNN) (C) Long Short-Term Memory (LSTM) (D) Autoencoder 49. Which technique is used to handle categorical variables in machine learning? (A) Principal Component Analysis (PCA) (B) Label Encoding (C) Gradient Descent (D) Ridge Regression 50. Which evaluation metric is appropriate for evaluating a regression model’s performance? (A) F1-score (B) Mean Absolute Error (MAE) (C) Precision-Recall AUC (D) ROC AUC Curve