Artificial Intelligence and Machine Learning MCQs

1. Which of the following is not a type of artificial intelligence? A) Narrow AI B) General AI C) Super AI D) Applied AI Answer: C 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 improve the performance of reinforcement learning algorithms D) To predict an output variable based on input data Answer: D 3. Which technique is used to reduce the dimensionality of data in machine learning? A) Principal Component Analysis (PCA) B) Linear Regression C) K-nearest neighbors (KNN) D) Naive Bayes Answer: A 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 Answer: D 5. Which algorithm is used for anomaly detection? A) Decision Trees B) K-means clustering C) Support Vector Machines (SVM) D) Isolation Forest Answer: D 6. Which technique is suitable for handling non-linear decision boundaries? A) Logistic Regression B) Decision Trees C) Linear Discriminant Analysis (LDA) D) Support Vector Machines (SVM) Answer: D 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 Answer: B 8. Which neural network architecture is typically used for image recognition tasks? A) Recurrent Neural Network (RNN) B) Convolutional Neural Network (CNN) C) Long Short-Term Memory (LSTM) D) Autoencoder Answer: B 9. Which evaluation metric is commonly used for imbalanced datasets? A) Accuracy B) Precision-Recall AUC C) F1-score D) Mean Squared Error (MSE) Answer: C 10. Which technique is used to preprocess text data in natural language processing? A) Principal Component Analysis (PCA) B) One-hot encoding C) Stemming and Lemmatization D) K-means clustering Answer: C 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 Answer: D 12. Which algorithm is suitable for both classification and regression tasks? A) Random Forest B) K-means clustering C) Gradient Descent D) K-nearest neighbors (KNN) Answer: A 13. Which type of machine learning algorithm is most appropriate for predicting stock prices? A) Supervised Learning B) Unsupervised Learning C) Reinforcement Learning D) Semi-supervised Learning Answer: A 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 Answer: A 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 Answer: C 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 Answer: D 17. What does the term “ensemble learning” refer to? A) A method where multiple models are combined to improve performance B) Learning from a large dataset in a distributed computing environment C) Using deep learning models for prediction tasks D) Training multiple models with different hyperparameters Answer: A 18. What does the activation function in a neural network do? A) It determines the number of layers in the network B) It normalizes the input data C) It introduces non-linearity into the network D) It adjusts the learning rate during training Answer: C 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) Answer: B 20. In which scenario would unsupervised learning be most appropriate? A) Spam email detection B) Sentiment analysis of customer reviews C) Customer segmentation based on purchasing behavior D) Predicting housing prices based on historical data Answer: C 21. Which technique is used for feature selection in machine learning? A) Recursive Feature Elimination (RFE) B) Hyperparameter tuning C) Learning rate adjustment D) Model validation Answer: A 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) Answer: B 23. Which method is used to handle missing data in a dataset? A) Data augmentation B) Data imputation C) Data normalization D) Data standardization Answer: B 24. Which approach is used to reduce the variance of a machine learning model? A) L1 Regularization B) L2 Regularization C) Dropout D) Batch Normalization Answer: C 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 Answer: A 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 Answer: B 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 Answer: B 28. Which type of learning algorithm does not require labeled training data? A) Supervised Learning B) Unsupervised Learning C) Reinforcement Learning D) Semi-supervised Learning Answer: B 29. Which technique is used for reducing the dimensionality of sparse data? A) Principal Component Analysis (PCA) B) Singular Value Decomposition (SVD) C) Linear Discriminant Analysis (LDA) D) Ridge Regression Answer: B 30. In which scenario would you use a Recurrent Neural Network (RNN) rather than a Feedforward Neural Network (FNN)? A) Image classification B) Text translation C) Handwritten digit recognition D) Speech recognition Answer: B 31. Which technique is used for model evaluation when dealing with imbalanced classes? A) ROC AUC Curve B) Mean Absolute Error (MAE) C) R-squared (R2) score D) Root Mean Squared Error (RMSE) Answer: A 32. What is the primary challenge in training deep neural networks? A) Overfitting B) Underfitting C) Gradient vanishing or exploding D) High computational cost Answer: C 33. Which method is used for hyperparameter optimization in machine learning? A) Grid Search B) K-means clustering C) Bagging D) Cross-validation Answer: A 34. Which technique is used for data augmentation in computer vision tasks? A) Principal Component Analysis (PCA) B) Dropout C) Batch Normalization D) Image rotation and flipping Answer: D 35. Which algorithm is used for clustering in unsupervised learning? A) Linear Regression B) K-nearest neighbors (KNN) C) K-means clustering D) Gradient Boosting Answer: C 36. Which method is used to handle multicollinearity in linear regression? A) Ridge Regression B) Logistic Regression C) Lasso Regression D) Naive Bayes Answer: A 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 Answer: C 38. What is the primary objective of the “bagging” technique in machine learning? A) To reduce bias in the model B) To reduce variance in the model C) To speed up convergence D) To preprocess the input data Answer: B 39. Which technique is used for anomaly detection in network security? A) Apriori algorithm B) Isolation Forest C) Linear Discriminant Analysis (LDA) D) AdaBoost Answer: B 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 Answer: B 41. Which approach is used for sentiment analysis of text data? A) Word2Vec B) Hashing Vectorizer C) Bag of Words (BoW) D) Support Vector Machines (SVM) Answer: C 42. What is the purpose of the “ReLU” activation function in a neural network? A) To normalize the input data B) To introduce non-linearity C) To convert predictions into probabilities D) To reduce the computational cost Answer: B 43. Which technique is used for time series data forecasting with long-term dependencies? A) Autoencoder B) Long Short-Term Memory (LSTM) C) Principal Component Analysis (PCA) D) AdaBoost Answer: B 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 complex and learns noise from the training data C) When a model is too simple to capture the underlying patterns in the data D) When a model does not converge during training Answer: C 45. Which algorithm is used for recommendation systems based on collaborative filtering? A) K-means clustering B) Singular Value Decomposition (SVD) C) Decision Trees D) Naive Bayes Answer: B 46. Which technique is used to handle class imbalance in classification problems? A) Gradient Boosting B) Naive Bayes C) SMOTE (Synthetic Minority Over-sampling Technique) D) L1 Regularization Answer: C 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 Answer: C 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) Autoencoder D) Long Short-Term Memory (LSTM) Answer: C 49. Which technique is used to handle categorical variables in machine learning? A) Label Encoding B) Principal Component Analysis (PCA) C) Gradient Descent D) Ridge Regression Answer: A 50. Which evaluation metric is appropriate for evaluating a regression model’s performance? A) F1-score B) Precision-Recall AUC C) Mean Absolute Error (MAE) D) ROC AUC Curve Answer: C

Artificial Intelligence and Machine Learning MCQs

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