Assess students' understanding of fundamental distinctions between supervised and unsupervised learning, typical tasks (classification, regression, clustering), and core evaluation metrics (accuracy, precision, recall, F1 score, confusion matrix, silhouette score). Test when to apply each paradigm, how to select and interpret basic metrics for classification versus clustering, and simple reasoning about metric trade-offs and class imbalance; suitable for graduate learners at an introductory level.