The answer is that retaining too few principal components is the most likely cause of poor classifier performance after PCA. This happens because PCA is an unsupervised technique that projects data onto components capturing maximum variance, but variance does not always align with class separability; discarding components with lower variance may eliminate subtle but critical features that the classifier needs to distinguish between classes, leading to underfitting. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding that PCA’s variance-based ranking is class-agnostic, and a common trap is assuming more dimensionality reduction always improves generalization. Remember the memory tip: “Variance is not the same as class relevance”—just because a component explains less overall variance doesn’t mean it’s useless for your classifier.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Too many principal components were retained, including noise
Why it's wrong here
May cause overfitting, but not most likely.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
PCA works by computing eigenvectors of the covariance matrix; the eigenvalues indicate the variance explained by each component. Retaining too few components discards dimensions that may contain class-discriminative information, especially if the variance structure does not align with class separability. In practice, using a cumulative explained variance threshold (e.g., 95%) or a scree plot helps balance dimensionality reduction with information retention.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
Question Discussion
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