MCQs

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Overall MCQs for Reference

Which algorithm is primarily used for grouping unlabeled data into clusters?
a. Linear Regression
b. K-Means Clustering
c. Decision Tree
d. Logistic Regression
Answer: b. K-Means Clusteringsimplilearn+2
What learning paradigm involves learning from labeled data?
a. Supervised Learning
b. Unsupervised Learning
c. Reinforcement Learning
d. Semi-Supervised Learning
Answer: a. Supervised Learningtechtarget+2
Which AI learning algorithm is best for predicting binary outcomes?
a. Linear Regression
b. Naive Bayes
c. Logistic Regression
d. K-Means
Answer: c. Logistic Regressiongeeksforgeeks+2
What is the main purpose of a Decision Tree in AI?
a. Regression analysis
b. Clustering
c. Classification and regression
d. Dimensionality reduction
Answer: c. Classification and regressioncoursera+2
Which of the following is an example of an ensemble learning algorithm?
a. Decision Tree
b. RAND Algorithm
c. Random Forest
d. Naive Bayes
Answer: c. Random Forestsimplilearn+2
Which learning algorithm estimates a value function to maximize future rewards?
a. K-Means
b. Reinforcement Learning
c. Support Vector Machine
d. Linear Regression
Answer: b. Reinforcement Learningtechtarget+2
Which algorithm is NOT typically used for unsupervised learning?
a. K-Means
b. Principal Component Analysis
c. Decision Tree
d. Autoencoder
Answer: c. Decision Treegeeksforgeeks+2
Support Vector Machines (SVM) are mainly used for:
a. Regression only
b. Classification and regression
c. Clustering
d. Feature selection
Answer: b. Classification and regressioncoursera+2
What kind of network is optimal for image recognition?
a. Linear Regression
b. Perceptron
c. Convolutional Neural Network (CNN)
d. Reinforcement Learning
Answer: c. Convolutional Neural Network (CNN)simplilearn+1
Which method is model-free in reinforcement learning?
a. SARSA
b. Q-Learning
c. Perceptron
d. Gradient Descent
Answer: b. Q-Learningtestbook+2
In which algorithm is the Gaussian distribution an assumption?
a. Principal Component Analysis
b. Gaussian Mixture Model
c. K-Means
d. Decision Tree
Answer: b. Gaussian Mixture Modeltechtarget+1
Which of the following finds frequent patterns in large data sets?
a. K-Means
b. Apriori Algorithm
c. Naive Bayes
d. Logistic Regression
Answer: b. Apriori Algorithmgeeksforgeeks
Which learning algorithm is designed for anomaly detection?
a. Isolation Forest
b. K-Means
c. Decision Tree
d. Perceptron
Answer: a. Isolation Forestgeeksforgeeks
Which learning strategy can utilize both labeled and unlabeled data?
a. Supervised
b. Unsupervised
c. Semi-Supervised
d. Reinforcement
Answer: c. Semi-Supervisedibm
Which is a dimensionality reduction algorithm?
a. Decision Tree
b. Principal Component Analysis
c. KNN
d. Random Forest
Answer: b. Principal Component Analysistechtarget+1
What is the purpose of a Perceptron?
a. Clustering
b. Anomaly Detection
c. Binary Classification
d. Association Rule Mining
Answer: c. Binary Classificationgeeksforgeeks
Which algorithm is commonly used for text classification?
a. Linear Regression
b. Naive Bayes
c. K-Means
d. Apriori
Answer: b. Naive Bayescoursera+1
Which method uses ensemble bagging?
a. Random Forest
b. KMeans
c. Logistic Regression
d. Q-Learning
Answer: a. Random Forestinterviewkickstart+1
What learning paradigm uses action-reward feedback loops?
a. Supervised Learning
b. Semi-Supervised Learning
c. Reinforcement Learning
d. Deep Learning
Answer: c. Reinforcement Learningsimplilearn+1
Which clustering algorithm is density-based?
a. K-Means
b. DBSCAN
c. Decision Tree
d. Linear Regression
Answer: b. DBSCANgeeksforgeeks
Which method is known for high bias and low variance?
a. Decision Tree
b. Random Forest
c. Linear Regression
d. KNN
Answer: c. Linear Regressioncoursera+1
What describes Principal Component Analysis (PCA)?
a. Supervised Learning
b. Dimensionality Reduction
c. Classification
d. Reinforcement Learning
Answer: b. Dimensionality Reductiontechtarget+1
What is used to optimize neural networks?
a. Random Search
b. Gradient Descent
c. Clustering
d. Bagging
Answer: b. Gradient Descentsimplilearn+1
Which of the following is a policy optimization approach?
a. Q-Learning
b. Deep Q-Network
c. Policy Gradient
d. Isolation Forest
Answer: c. Policy Gradientsimplilearn+1
Which deep learning architecture adapts spatial hierarchies in data?
a. Perceptron
b. Logistic Regression
c. Convolutional Neural Network
d. KNN
Answer: c. Convolutional Neural Networkgeeksforgeeks
Which of the following is NOT a classification algorithm?
a. Decision Tree
b. KNN
c. Naive Bayes
d. K-Means
Answer: d. K-Meanscoursera+1
Which algorithm is best for market basket analysis?
a. Apriori Algorithm
b. CNN
c. SVM
d. Isolation Forest
Answer: a. Apriori Algorithmgeeksforgeeks
Which technique is used for regularization in neural networks?
a. Pooling
b. Dropout
c. Bagging
d. Clustering
Answer: b. Dropoutsimplilearn
Which of the following is used in deep reinforcement learning?
a. Deep Q-Network (DQN)
b. Apriori
c. PCA
d. LARS
Answer: a. Deep Q-Network (DQN)simplilearn+1
Which algorithm searches by simulation for optimal decisions?
a. Monte Carlo Tree Search
b. DBSCAN
c. Isolation Forest
d. Apriori Algorithm
Answer: a. Monte Carlo Tree Searchgeeksforgeeks
Which is a form of unsupervised neural network learning?
a. Perceptron
b. Autoencoder
c. Q-Learning
d. Decision Tree
Answer: b. Autoencodertechtarget+1
What is the key idea behind KNN (K-Nearest Neighbors)?
a. Ensemble voting
b. Instance-based learning
c. Policy optimization
d. Clustering centroids
Answer: b. Instance-based learningtechtarget+1
Which algorithm is used for face recognition and feature detection?
a. CNN
b. DBSCAN
c. PCA
d. Linear Regression
Answer: a. CNNgeeksforgeeks
Which type of neural network is primarily used for sequence data?
a. CNN
b. Perceptron
c. RNN (Recurrent Neural Network)
d. Q-Learning
Answer: c. RNN (Recurrent Neural Network)geeksforgeeks
What is the goal of k-means++ initialization?
a. Reduce model bias
b. Improve centroid initialization
c. Reduce overfitting
d. Flatten data features
Answer: b. Improve centroid initializationgeeksforgeeks
Which method is NOT an association rule mining algorithm?
a. FP-Growth
b. Apriori
c. ECLAT
d. KNN
Answer: d. KNNgeeksforgeeks
Which algorithm is robust against outliers for anomaly detection?
a. Isolation Forest
b. SVM
c. Linear Regression
d. K-Means
Answer: a. Isolation Forestgeeksforgeeks
What approach is commonly used in self-supervised learning?
a. Data labeling by experts
b. Training with input reconstruction
c. Policy optimization
d. Ensemble methods
Answer: b. Training with input reconstructionibm+1
What optimization does AdaBoost use?
a. Random selection
b. Weight adjustment for misclassified instances
c. Instance-based voting
d. Ensemble bagging
Answer: b. Weight adjustment for misclassified instancessimplilearn
Which of the following is used for regression tasks?
a. Decision Tree
b. K-Means
c. Apriori
d. ECLAT
Answer: a. Decision Treetechtarget+1
Which algorithm improves overfitting using bootstrapped datasets?
a. Random Forest
b. Perceptron
c. SVM
d. Isolation Forest
Answer: a. Random Forestcoursera+1
What is SARSA primarily used for?
a. Clustering
b. Q-Learning stabilization
c. On-policy reinforcement learning
d. Feature extraction
Answer: c. On-policy reinforcement learningtechtarget
Which is a feature detection algorithm in AI?
a. SIFT
b. KNN
c. Random Forest
d. Isolation Forest
Answer: a. SIFTgeeksforgeeks
Which clustering algorithm builds hierarchy of clusters?
a. K-Means
b. Gaussian Mixture Model
c. Hierarchical Clustering
d. DBSCAN
Answer: c. Hierarchical Clusteringgeeksforgeeks
What is the main advantage of Bagging?
a. High bias
b. Reduce variance
c. Improve centroid selection
d. Increase error
Answer: b. Reduce varianceinterviewkickstart+1
Which method is used for object detection in images?
a. CNN
b. Random Forest
c. DBSCAN
d. Apriori
Answer: a. CNNgeeksforgeeks
What is the main purpose of reinforcement learning?
a. Classification
b. Regression
c. Policy learning via rewards
d. Clustering
Answer: c. Policy learning via rewardssimplilearn+1
What does an autoencoder attempt to do?
a. Classify new data
b. Discover associations
c. Reconstruct its input
d. Maximize reward
Answer: c. Reconstruct its inputtechtarget+1
Which unsupervised method is used for reducing data redundancy?
a. K-Means
b. Principal Component Analysis
c. Random Forest
d. Decision Tree
Answer: b. Principal Component Analysistechtarget+1
What technique is at the core of deep learning?
a. Decision Tree
b. Linear Regression
c. Neural Networks
d. FP-Growth
Answer: c. Neural Networkssimplilearn+2

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