Quick Answer
The correct statistic to compute is a correlation matrix using Pearson’s correlation coefficient. This directly addresses the need for detecting multicollinearity with a correlation matrix because it reveals pairwise linear relationships between numeric features—values near +1 or -1 signal high collinearity that can destabilize regression models. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your ability to choose the right diagnostic tool for numeric data, with a common trap being to select variance inflation factor (VIF) even though VIF requires more observations than predictors to be reliable, or to confuse covariance with correlation, which is scale-dependent. The correlation matrix is the quickest first pass for multicollinearity checks, and the exam often presents it as the go-to method when all columns are numeric and the sample size is limited. Memory tip: think “Pearson pairs” for numeric predictors—if two features dance together, the matrix will show it.
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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.
Refer to the exhibit. A data scientist examines a sample of data and notices that all columns are numeric. The scientist wants to check for multicollinearity. Which statistic should be computed from this sample?
Correlation matrix (Pearson)
A correlation matrix can reveal high pairwise correlations.
Chi-square test of independence
Why wrong: Chi-square is for categorical variables.
Variance Inflation Factor (VIF)
Why wrong: VIF is not reliable with such a small sample (5 rows).
Covariance matrix
Why wrong: Covariance is scale-dependent and harder to interpret.
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 (Pearson)
Option A is correct because the correlation matrix shows pairwise Pearson correlations, which can indicate high collinearity. Option B is wrong because VIF requires more variables than observations. Option C is wrong because chi-square is for categorical. Option D is wrong because covariance alone is scale-dependent.
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.
Correlation matrix (Pearson)
Why this is correct
A correlation matrix can reveal high pairwise correlations.
Related concept
Read the scenario before looking for a memorised answer.
Chi-square test of independence
Why it's wrong here
Chi-square is for categorical variables.
Variance Inflation Factor (VIF)
Why it's wrong here
VIF is not reliable with such a small sample (5 rows).
Covariance matrix
Why it's wrong here
Covariance is scale-dependent and harder to interpret.
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
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
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: Correlation matrix (Pearson) — Option A is correct because the correlation matrix shows pairwise Pearson correlations, which can indicate high collinearity. Option B is wrong because VIF requires more variables than observations. Option C is wrong because chi-square is for categorical. Option D is wrong because covariance alone is scale-dependent.
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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