NSCT – Unsupervised Learning MCQs 20 min Score: 0 Attempted: 0/20 Subscribe 1. . Unsupervised learning is: (A) Training models with labeled data only (B) A type of machine learning where the model is trained on unlabeled data to find patterns or structure (C) Compressing labeled datasets (D) Backup onlyShow All Answers 2. . The main goal of unsupervised learning is to: (A) Encrypt patterns (B) Discover hidden patterns, clusters, or relationships in data (C) Compress datasets (D) Backup only 3. . Clustering in unsupervised learning is: (A) Compressing clusters (B) Encrypting clusters (C) Grouping similar data points into clusters (D) Backup only 4. . K-Means is: (A) Backup only (B) A supervised learning algorithm (C) Encryption algorithm (D) A clustering algorithm that partitions data into K clusters based on similarity 5. . Hierarchical clustering builds: (A) A flat label set (B) A hierarchy of clusters using either agglomerative or divisive methods (C) An encryption tree (D) Backup only 6. . Dimensionality reduction in unsupervised learning is: (A) Compressing datasets (B) Encrypting features (C) Reducing the number of input variables to simplify data and improve analysis (D) Backup only 7. . Principal Component Analysis (PCA) is used to: (A) Reduce dimensionality while preserving variance in data (B) Encrypt principal components (C) Compress features only (D) Backup only 8. . Independent Component Analysis (ICA) is used for: (A) Separating a multivariate signal into independent non-Gaussian components (B) Encrypting signals (C) Compressing signals (D) Backup only 9. . Anomaly detection in unsupervised learning is: (A) Encrypting anomalies (B) Identifying unusual data points that differ significantly from the majority (C) Compressing anomalies (D) Backup only 10. . DBSCAN is: (A) A density-based clustering algorithm that identifies clusters and outliers (B) A supervised regression algorithm (C) Encryption method (D) Backup only 11. . Autoencoders are: (A) Encrypting autoencoders (B) Neural networks used for unsupervised feature learning and dimensionality reduction (C) Compressing neural networks (D) Backup only 12. . Silhouette score in clustering measures: (A) How well data points are clustered, indicating cohesion and separation (B) Encrypting clusters (C) Compressing clusters (D) Backup only 13. . Unsupervised learning is useful for: (A) Compressing patterns (B) Encrypting marketing data (C) Market segmentation, anomaly detection, pattern discovery, and dimensionality reduction (D) Backup only 14. . Cosine similarity is used in unsupervised learning to: (A) Encrypt similarity (B) Measure similarity between two vectors (C) Compress vectors (D) Backup only 15. . Agglomerative clustering starts with: (A) One big cluster (B) Each data point as its own cluster and merges them iteratively (C) Encrypting clusters (D) Backup only 16. . Divisive clustering starts with: (A) All data points in one cluster and splits them iteratively (B) Each point as a cluster (C) Encrypting divisions (D) Backup only 17. . Feature scaling is important in unsupervised learning because: (A) Compressing features (B) Encrypting features (C) Distance-based algorithms like K-Means are sensitive to feature magnitudes (D) Backup only 18. . The elbow method is used to: (A) Determine the optimal number of clusters in K-Means (B) Encrypt K-values (C) Compress clusters (D) Backup only 19. . t-SNE is a technique used for: (A) Visualizing high-dimensional data in 2D or 3D (B) Encrypting high-dimensional data (C) Compressing features (D) Backup only 20. . The main purpose of unsupervised learning is to: (A) Discover hidden structure, patterns, or relationships in unlabeled data (B) Encrypt all data (C) Compress datasets (D) Backup only
