Unsupervised Learning and Deployment

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1. Which explanation matches clustering: find groups without target labels?A. The first principal component captures the largest variance under the PCA formulation, and subsequent components capture remaining variance while being orthogonal to earlier ones.B. Package the preprocessing state and feature order with the model.C. Use a split that reflects future deployment, such as earlier dates for training and later dates for validation and testing.D. K-means alternates between assigning points to centers and updating the centers.

2. Which explanation matches pca: compress features into directions?A. K-means alternates between assigning points to centers and updating the centers.B. Package the preprocessing state and feature order with the model.C. The first principal component captures the largest variance under the PCA formulation, and subsequent components capture remaining variance while being orthogonal to earlier ones.D. Use a split that reflects future deployment, such as earlier dates for training and later dates for validation and testing.

3. Which explanation matches deploy and monitor a complete prediction pipeline?A. K-means alternates between assigning points to centers and updating the centers.B. Package the preprocessing state and feature order with the model.C. The first principal component captures the largest variance under the PCA formulation, and subsequent components capture remaining variance while being orthogonal to earlier ones.D. Use a split that reflects future deployment, such as earlier dates for training and later dates for validation and testing.

4. Could a low-variance feature still be important for predicting a rare event?A. Yes. Predictive relevance and variance are different concepts. A subtle feature may distinguish the rare event even if it contributes little overall variation.B. Spending could dominate the geometry unless scaling or a deliberately chosen distance accounts for the difference.C. Compare representative held-out performance, verify input/output compatibility, and confirm latency, resource use, and rollback behavior.D. The report should let another person reproduce the comparison and understand where the system may fail. Keep the final test separate from the choices that selected the model.

5. Prepare a project report with the target, available features, split rule, baseline, chosen pipeline, metrics, difficult cases, and update plan.A. Spending could dominate the geometry unless scaling or a deliberately chosen distance accounts for the difference.B. Yes. Predictive relevance and variance are different concepts. A subtle feature may distinguish the rare event even if it contributes little overall variation.C. Compare representative held-out performance, verify input/output compatibility, and confirm latency, resource use, and rollback behavior.D. The report should let another person reproduce the comparison and understand where the system may fail. Keep the final test separate from the choices that selected the model.

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Unsupervised Learning and Deployment

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