Quick Answer
The answer is Cramér's V, along with the chi-square test of independence and mutual information. These three are appropriate because they are specifically designed to test association between categorical features and a binary target, where both variables are categorical in nature. The chi-square test evaluates whether the observed frequencies differ from expected frequencies under independence, Cramér's V provides a normalized effect size based on chi-square, and mutual information captures any non-linear dependencies without distributional assumptions. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish between statistical tests for different variable types—a common trap is selecting ANOVA or Pearson correlation, which are reserved for continuous variables. A useful memory tip: for categorical-categorical association, remember the three C’s—Chi-square, Cramér’s V, and Correlation (mutual information).
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.
A data scientist is analyzing a dataset with several categorical features and a binary target. The scientist wants to check for association between each categorical feature and the target. Which THREE statistical tests are appropriate?
ANOVA
Why wrong: Used for comparing means across categories, not for categorical-categorical.
Pearson correlation coefficient
Why wrong: For continuous variables only.
Chi-square test of independence
Tests association between two categorical variables.
Mutual information
Captures dependence between categorical variables.
Cramér's V
Measures association strength based on chi-square.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Chi-square test of independence
Options A, B, and D are correct. Chi-square test of independence is for categorical-categorical association. Cramér's V is a measure of association based on chi-square. Mutual information is a non-parametric measure that can capture non-linear dependencies. Option C is wrong because ANOVA is for categorical vs continuous. Option E is wrong because Pearson correlation is for continuous variables.
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.
ANOVA
Why it's wrong here
Used for comparing means across categories, not for categorical-categorical.
Pearson correlation coefficient
Why it's wrong here
For continuous variables only.
Chi-square test of independence
Why this is correct
Tests association between two categorical variables.
Related concept
Read the scenario before looking for a memorised answer.
Mutual information
Why this is correct
Captures dependence between categorical variables.
Related concept
Read the scenario before looking for a memorised answer.
Cramér's V
Why this is correct
Measures association strength based on chi-square.
Related concept
Read the scenario before looking for a memorised answer.
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.
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: Chi-square test of independence — Options A, B, and D are correct. Chi-square test of independence is for categorical-categorical association. Cramér's V is a measure of association based on chi-square. Mutual information is a non-parametric measure that can capture non-linear dependencies. Option C is wrong because ANOVA is for categorical vs continuous. Option E is wrong because Pearson correlation is for continuous variables.
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.
About these practice questions
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Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is analyzing a dataset with a large number of categorical features. The target variable is binary. Which technique should the scientist use to assess the relationship between each categorical feature and the target?
hardWhy D: The chi-square test of independence is appropriate for testing association between categorical features and a binary target. ANOVA is for continuous target. Mutual information measures dependency but is not a hypothesis test. Point-biserial correlation is for continuous and binary. Cramér's V is a measure of association after chi-square.
Variation 2. A machine learning engineer is analyzing a dataset with a mix of categorical and numerical features. The engineer wants to understand the correlation between categorical features and the target variable. Which statistical test is most appropriate for measuring association between a categorical feature and a binary target?
mediumWhy C: Option C is correct because the Chi-squared test of independence is used to determine if there is a significant association between two categorical variables, which is applicable here. Option A is wrong because Pearson correlation is for continuous variables. Option B is wrong because ANOVA is for comparing means across groups, but assumes continuous target. Option D is wrong because Mutual Information can be used but is not a statistical test with a p-value.
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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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