The correct metric to compute is the ANOVA F-statistic between feature X and the target. This is because the ANOVA F-statistic directly tests whether the means of the feature across the three classes (A, B, C) are significantly different, quantifying how well the feature separates the groups—even when distributions are bimodal or unimodal. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of feature selection for multiclass targets, where ANOVA is preferred over chi-square (which requires categorical features) or mutual information (which measures dependency but not separability). A common trap is confusing variance-based metrics with the F-statistic; remember that ANOVA compares between-group variance to within-group variance. Memory tip: "ANOVA for means, chi-square for counts"—if your target is categorical and feature is continuous, ANOVA is your go-to for separability.
MLS-C01 Exploratory Data Analysis Practice Question
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 analyst is examining a dataset with a target variable that has three classes: A, B, C. They plot the distribution of a feature 'X' for each class and notice that for classes A and B, the distributions are bimodal, while for class C it is unimodal. They want to assess whether feature 'X' is useful for separating the classes. Which of the following metrics should they compute to quantify the separability?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
ANOVA F-statistic between feature X and the target.
Option A is correct because ANOVA F-statistic tests if means across groups are significantly different. Option B is wrong because chi-square is for categorical features. Option C is wrong because mutual information is for feature selection but doesn't directly test separability. Option D is wrong because variance ratio is not standard.
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.
✓
ANOVA F-statistic between feature X and the target.
Why this is correct
ANOVA tests if the mean of X differs across classes.
Related concept
Read the scenario before looking for a memorised answer.
✗
Variance ratio (between-group variance / within-group variance).
Why it's wrong here
Variance ratio is related to ANOVA but not a standard metric; the F-statistic is the proper one.
✗
Chi-square test of independence.
Why it's wrong here
Chi-square requires categorical variables.
✗
Mutual information between X and the target.
Why it's wrong here
Mutual information measures dependency but does not specifically assess separability.
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: ANOVA F-statistic between feature X and the target. — Option A is correct because ANOVA F-statistic tests if means across groups are significantly different. Option B is wrong because chi-square is for categorical features. Option C is wrong because mutual information is for feature selection but doesn't directly test separability. Option D is wrong because variance ratio is not standard.
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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Question Discussion
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