- A
ANOVA F-statistic between feature X and the target.
Correct. The ANOVA F-statistic tests whether the means of feature X differ significantly across classes A, B, and C, which is a direct measure of separability.
- B
Variance ratio (between-group variance / within-group variance).
Why wrong: Incorrect. While the concept of variance ratio (between-group / within-group) underlies the F-statistic, 'variance ratio' is not a standard metric name; the correct term is ANOVA F-statistic.
- C
Chi-square test of independence.
Why wrong: Incorrect. The chi-square test of independence is used for categorical features, but feature X is continuous, so this test is not appropriate.
- D
Mutual information between X and the target.
Why wrong: Incorrect. Mutual information quantifies the amount of information shared between X and the target, but it does not directly measure separability in terms of group variance differences; the F-statistic is more appropriate here.
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 choices
Why each option matters
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 the ANOVA F-statistic measures the ratio of between-group variance to within-group variance, directly quantifying separability. Option B is wrong because 'variance ratio' is not the standard name; the correct metric is the F-statistic. Option C is wrong because the chi-square test is for categorical features, not continuous ones like feature X. Option D is wrong because mutual information measures dependency but does not specifically test separability in terms of variance between 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.
- ✓
ANOVA F-statistic between feature X and the target.
Why this is correct
Correct. The ANOVA F-statistic tests whether the means of feature X differ significantly across classes A, B, and C, which is a direct measure of separability.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Variance ratio (between-group variance / within-group variance).
Why it's wrong here
Incorrect. While the concept of variance ratio (between-group / within-group) underlies the F-statistic, 'variance ratio' is not a standard metric name; the correct term is ANOVA F-statistic.
- ✗
Chi-square test of independence.
Why it's wrong here
Incorrect. The chi-square test of independence is used for categorical features, but feature X is continuous, so this test is not appropriate.
- ✗
Mutual information between X and the target.
Why it's wrong here
Incorrect. Mutual information quantifies the amount of information shared between X and the target, but it does not directly measure separability in terms of group variance differences; the F-statistic is more appropriate here.
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.
Quick reference
IPv4 Address Class Summary
| Class | First Octet Range | Default Mask | Networks | Hosts per Network |
|---|---|---|---|---|
| A | 1–126 | /8 (255.0.0.0) | 126 | 16,777,214 |
| B | 128–191 | /16 (255.255.0.0) | 16,384 | 65,534 |
| C | 192–223 | /24 (255.255.255.0) | 2,097,152 | 254 |
| D | 224–239 | N/A | Multicast groups | — |
| E | 240–255 | N/A | Reserved / experimental | — |
127.x.x.x is reserved for loopback. Modern networks use CIDR (classless) rather than classful addressing.
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.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
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 the ANOVA F-statistic measures the ratio of between-group variance to within-group variance, directly quantifying separability. Option B is wrong because 'variance ratio' is not the standard name; the correct metric is the F-statistic. Option C is wrong because the chi-square test is for categorical features, not continuous ones like feature X. Option D is wrong because mutual information measures dependency but does not specifically test separability in terms of variance between 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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jun 20, 2026
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