Quick Answer
The answer is F1 score with macro or micro averaging, accuracy, and the confusion matrix. These three evaluation metrics for multi-class classification are appropriate because they each address different aspects of model performance: accuracy provides a simple overall correctness rate, the confusion matrix reveals per-class true positives, false positives, and misclassifications, and the F1 score with macro or micro averaging balances precision and recall across all classes, handling class imbalance effectively. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish classification metrics from regression metrics—a common trap is confusing mean squared error (regression) or precision-recall curves (binary classification) with multi-class evaluation. Remember that for multi-class problems, you need metrics that aggregate across all classes, not just one-vs-rest comparisons. A useful memory tip: think "MAC" for multi-class—Macro F1, Accuracy, and Confusion matrix.
This MLS-C01 practice question tests your understanding of modeling. 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.
Which THREE evaluation metrics are appropriate for a multi-class classification problem? (Choose 3.)
Confusion matrix.
Confusion matrix provides per-class performance.
Accuracy.
Accuracy is a straightforward metric for multi-class.
Mean squared error.
Why wrong: MSE is for regression problems.
Precision-recall curve.
Why wrong: PR curve is typically used for binary classification.
F1 score (macro/micro).
F1 score can be averaged across classes.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Confusion matrix.
Option A is correct because accuracy is common for multi-class. Option C is correct because confusion matrix gives detailed per-class performance. Option D is correct because F1 score macro/micro averaging is used. Option B is wrong because mean squared error is for regression. Option E is wrong because precision-recall curve is typically for binary.
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.
Confusion matrix.
Why this is correct
Confusion matrix provides per-class performance.
Related concept
Read the scenario before looking for a memorised answer.
Accuracy.
Why this is correct
Accuracy is a straightforward metric for multi-class.
Related concept
Read the scenario before looking for a memorised answer.
Mean squared error.
Why it's wrong here
MSE is for regression problems.
Precision-recall curve.
Why it's wrong here
PR curve is typically used for binary classification.
F1 score (macro/micro).
Why this is correct
F1 score can be averaged across classes.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Confusion matrix. — Option A is correct because accuracy is common for multi-class. Option C is correct because confusion matrix gives detailed per-class performance. Option D is correct because F1 score macro/micro averaging is used. Option B is wrong because mean squared error is for regression. Option E is wrong because precision-recall curve is typically for binary.
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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