Quick Answer
The answer is the F1 score. This metric is the most appropriate for imbalanced classification because it balances precision and recall, providing a single score that penalizes models that sacrifice one for the other—critical when negative cases (like the 100 negative reviews) are vastly outnumbered. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding that accuracy is a trap: predicting all reviews as positive yields 99% accuracy but zero utility. AUC-ROC can also be misleading here, as it may appear strong even when the model fails to identify the minority class. A common exam trick is offering accuracy or AUC-ROC as distractors; remember that F1 directly addresses the class imbalance by focusing on the minority class performance. Memory tip: For imbalanced data, think “F1 for the few”—it forces the model to catch the rare but important cases.
This MLS-C01 practice question tests your understanding of modeling. 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 company is building a model to classify customer reviews as positive or negative. The dataset has 10,000 positive and 100 negative reviews. Which metric is most appropriate for evaluating model performance?
F1 score.
F1 score considers both precision and recall, good for imbalance.
Accuracy.
Why wrong: Accuracy is not suitable for imbalanced classes.
Mean squared error.
Why wrong: MSE is for regression, not classification.
AUC-ROC.
Why wrong: AUC-ROC may be too optimistic for severe imbalance.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
F1 score.
Option B is correct because F1 score balances precision and recall, important for imbalanced datasets. Option A is wrong because accuracy can be misleading (e.g., 99% by predicting all positive). Option C is wrong because AUC-ROC can be optimistic for imbalanced data. Option D is wrong because mean squared error is for regression.
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.
F1 score.
Why this is correct
F1 score considers both precision and recall, good for imbalance.
Related concept
Read the scenario before looking for a memorised answer.
Accuracy.
Why it's wrong here
Accuracy is not suitable for imbalanced classes.
Mean squared error.
Why it's wrong here
MSE is for regression, not classification.
AUC-ROC.
Why it's wrong here
AUC-ROC may be too optimistic for severe imbalance.
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: F1 score. — Option B is correct because F1 score balances precision and recall, important for imbalanced datasets. Option A is wrong because accuracy can be misleading (e.g., 99% by predicting all positive). Option C is wrong because AUC-ROC can be optimistic for imbalanced data. Option D is wrong because mean squared error is for regression.
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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