PCA MCQ Test
15 Questions
Time: 25 mins
Intermediate
Principal Component Analysis (PCA) MCQ Test
Practice how PCA finds new axes capturing maximum variance, and how to use it for dimensionality reduction in ML pipelines.
Easy: 5 Q
Medium: 6 Q
Hard: 4 Q
Principal Component Analysis (PCA): MCQ Practice
PCA is a foundational technique for reducing dimensionality, visualizing high‑dimensional data and decorrelating features. These questions reinforce both intuition and math.
Rotate to Maximize Variance
PCA finds orthogonal directions (principal components) along which the data variance is maximized.
PCA Workflow
Center (and optionally scale) Data → Compute Covariance Matrix → Eigen Decomposition / SVD → Project onto Top Components