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Question 386 of 1,672
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MLS-C01 Modeling Practice Question

A data scientist is using principal component analysis (PCA) for dimensionality reduction before training a classifier. The classifier's performance on the test set is poor. What is the most likely cause?

⚠ Common exam trap

AWS often tests the misconception that PCA always improves classifier performance by removing noise, but the trap here is that candidates may overlook the risk of underfitting when too few components are retained, especially when the discarded variance contains critical discriminative features.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Too few principal components were retained, losing important information

C is correct because PCA is an unsupervised dimensionality reduction technique that projects data onto principal components capturing the maximum variance. If too few components are retained, the reduced representation may discard features that are critical for the classifier to distinguish between classes, leading to poor test performance due to underfitting.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • The classifier is overfitting

    Why it's wrong here

    Test set performance poor indicates overfitting, but it's not directly caused by PCA.

  • The data was not scaled before applying PCA

    Why it's wrong here

    Scaling affects PCA but not necessarily poor classification.

  • Too few principal components were retained, losing important information

    Why this is correct

    Discards discriminative features.

  • Too many principal components were retained, including noise

    Why it's wrong here

    May cause overfitting, but not most likely.

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Last reviewed: Jun 30, 2026

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