Quick Answer
The answer is a correlation matrix with heatmap, as this technique directly quantifies linear relationships between numerical features by calculating pairwise Pearson correlation coefficients. When performing exploratory data analysis for the AWS Certified Machine Learning Specialty MLS-C01 exam, detecting multicollinearity among numerical features is critical because high correlations can destabilize regression models and inflate coefficient variances. A heatmap visualizes these correlations with color intensity, making it easy to spot pairs with absolute values above 0.7 or 0.8. The common trap here is confusing visualization tools: box plots show distribution spread, not relationships, while chi-square tests assess categorical associations, not numerical collinearity. Pair plots offer scatter plots but lack the quantitative measure needed for definitive detection. On the exam, remember that multicollinearity detection requires a numeric metric, not just a visual pattern. Memory tip: think “Correlation Matrix = Numbers + Colors” to distinguish it from purely visual pair plots.
This MLS-C01 practice question tests your understanding of exploratory data analysis. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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 performing EDA on a dataset with both numerical and categorical features. Which technique is best for detecting multicollinearity among numerical features?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Chi-square test of independence
Why wrong: Chi-square test is for categorical variables.
Box plots for each numerical feature
Why wrong: Box plots show distribution, not relationships between features.
Correlation matrix with heatmap
Correlation matrix shows pairwise linear correlations, indicating multicollinearity.
Pair plot
Why wrong: Pair plots are visual and not a quantitative measure of multicollinearity.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Correlation matrix with heatmap
Option B is correct because a correlation matrix quantifies linear relationships between numerical features. Option A is wrong because box plots show distribution, not relationships. Option C is wrong because chi-square test is for categorical associations. Option D is wrong because pair plots visualize scatter plots but not a quantitative measure of multicollinearity.
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.
Chi-square test of independence
Why it's wrong here
Chi-square test is for categorical variables.
Box plots for each numerical feature
Why it's wrong here
Box plots show distribution, not relationships between features.
Correlation matrix with heatmap
Why this is correct
Correlation matrix shows pairwise linear correlations, indicating multicollinearity.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Pair plot
Why it's wrong here
Pair plots are visual and not a quantitative measure of multicollinearity.
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.
Trap categories for this question
Command / output trap
Box plots show distribution, not relationships between features.
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.
Exploratory Data Analysis — study guide chapter
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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: Correlation matrix with heatmap — Option B is correct because a correlation matrix quantifies linear relationships between numerical features. Option A is wrong because box plots show distribution, not relationships. Option C is wrong because chi-square test is for categorical associations. Option D is wrong because pair plots visualize scatter plots but not a quantitative measure of multicollinearity.
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.
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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