How does PCA behave when dataset contains noise-dominant features?

PCA may incorrectly allocate components to noise if noise has high variance.

PCA assumes high variance corresponds to meaningful signal. If noise features exhibit high variance, PCA may prioritize them, leading to misleading components. This is why preprocessing and feature engineering are critical before PCA.