Free CompTIA DataX DY0-001 Actual Exam Questions - Question 14 Discussion

It’s A because PCA transforms data into fewer components without losing much info, which is exactly what dimensionality reduction is about. The other options are more about what you do with data after reducing it.

It’s A because PCA aims to reduce the number of features while preserving as much variance as possible. It’s not designed for tasks like classification, regression, or recommendation—those require different algorithms. PCA helps simplify data sets to make other analyses easier or more efficient, so dimensionality reduction fits perfectly here.

A imo, PCA is about simplifying data, not predicting or recommending.

A. PCA’s core purpose is to reduce dimensionality by capturing the most variance. It’s not really a method for predicting or recommending, so A fits best here.

A/B? PCA is mainly for reducing dimensions (A), but sometimes people confuse it with classification tasks (B). Definitely not C or D though.

Probably A since PCA is mainly used to reduce feature space.