The most strongly correlated original feature pair must be exactly PC1 and PC2.

Those three coefficients prove that a single component captures all variance in all 30 features.

Several size features carry overlapping variation, so the number of columns is not the same as the number of independent directions of variation.

PCA becomes unnecessary because highly correlated features already give perfect diagnosis predictions.

Reject the shortcut: correlation describes the relationship between the axes; a useful label-oriented view also depends on complementary variation and the observed class overlap.

Accept it only when both features have larger values in Malignant samples, because that establishes a causal link.

Replace it with the opposite rule: the least correlated pair always gives the clearest diagnosis separation.

Accept it: a large feature-feature correlation is equivalent to a large separation between diagnosis groups.