The answer is mutual information, the most appropriate technique for detecting nonlinear feature-target relationships in a dataset with 500 mixed numeric and categorical features. Unlike Pearson correlation, which only captures linear dependencies, mutual information measures the reduction in uncertainty about the target given the feature, making it sensitive to any kind of statistical dependence—including complex, nonlinear patterns. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish between correlation-based methods and information-theoretic approaches during exploratory data analysis. A common trap is choosing Pearson correlation because it is familiar, but remember that mutual information is the only option here that works for both numeric and categorical features without assuming linearity. Memory tip: “Mutual info catches any curve—Pearson only sees a line.”
MLS-C01 Exploratory Data Analysis Practice Question
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 performing EDA on a dataset with 500 features. The dataset has a mix of numeric and categorical features. The scientist wants to identify which features have a strong nonlinear relationship with the target variable. Which technique is most appropriate?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Calculate mutual information between each feature and the target.
Mutual information can capture any kind of dependency (including nonlinear) between features and target. Option A (Pearson correlation) only linear. Option B (Chi-squared test) is for categorical features. Option D (ANOVA) is for comparing means across groups.
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.
✗
Use ANOVA to compare feature means across target classes.
Why it's wrong here
ANOVA is for comparing means, not for measuring relationship strength.
✗
Compute Pearson correlation coefficients.
Why it's wrong here
Pearson correlation only captures linear relationships.
✓
Calculate mutual information between each feature and the target.
Why this is correct
Mutual information measures any dependency, including nonlinear.
Related concept
Read the scenario before looking for a memorised answer.
✗
Perform chi-squared tests for each feature.
Why it's wrong here
Chi-squared tests are for categorical variables only.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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
How to think about this question
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.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 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 — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Calculate mutual information between each feature and the target. — Mutual information can capture any kind of dependency (including nonlinear) between features and target. Option A (Pearson correlation) only linear. Option B (Chi-squared test) is for categorical features. Option D (ANOVA) is for comparing means across groups.
What should I do if I get this MLS-C01 question wrong?
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.
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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