This MLS-C01 practice question tests your understanding of exploratory data analysis. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 analyzing a dataset with 1,000 features. They suspect many features are redundant and want to reduce dimensionality before training a model. Which technique is most appropriate for identifying the most important features?
Apply principal component analysis (PCA) and select the top components
Why B is correct
Use L1 regularization (Lasso) to shrink coefficients to zero
Why wrong: Why C is wrong
Train a random forest and remove features with low importance
Why wrong: Why D is wrong
Compute the correlation matrix and remove features with high correlation
Why wrong: Why A is wrong
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Apply principal component analysis (PCA) and select the top components
Option B is correct because principal component analysis (PCA) is a dimensionality reduction technique that identifies the principal components capturing the most variance. Option A is wrong because correlation matrix only shows pairwise linear relationships, not importance. Option C is wrong because regularization can shrink coefficients but is not a dedicated dimensionality reduction technique. Option D is wrong because random forests can provide feature importance but are not a dimensionality reduction technique per se.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Apply principal component analysis (PCA) and select the top components
Why this is correct
Why B is correct
Related concept
Read the scenario before looking for a memorised answer.
Use L1 regularization (Lasso) to shrink coefficients to zero
Why it's wrong here
Why C is wrong
Train a random forest and remove features with low importance
Why it's wrong here
Why D is wrong
Compute the correlation matrix and remove features with high correlation
Why it's wrong here
Why A is wrong
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
The correct answer is: Apply principal component analysis (PCA) and select the top components — Option B is correct because principal component analysis (PCA) is a dimensionality reduction technique that identifies the principal components capturing the most variance. Option A is wrong because correlation matrix only shows pairwise linear relationships, not importance. Option C is wrong because regularization can shrink coefficients but is not a dedicated dimensionality reduction technique. Option D is wrong because random forests can provide feature importance but are not a dimensionality reduction technique per se.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 2026
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.
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