How does PCA behave under extreme multicollinearity conditions?

PCA becomes more effective as multicollinearity increases because redundancy is concentrated into fewer components.

In extreme multicollinearity, many features are linear combinations of others. This causes covariance matrix rank deficiency. PCA resolves this by collapsing correlated dimensions into a smaller number of orthogonal components that fully represent the dataset's intrinsic dimensionality.