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.
A data scientist is training a binary classification model on an imbalanced dataset where the positive class accounts for 5% of the data. The model achieves 95% accuracy but has a recall of only 10% for the positive class. Which metric should the data scientist primarily use to evaluate model performance?
RMSE
Why wrong: RMSE is for regression tasks.
F1 Score
F1 score considers both precision and recall.
Accuracy
Why wrong: Accuracy is not reliable for imbalanced data.
AUC-ROC
Why wrong: While useful, F1 is more direct for imbalanced 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
F1 Score
The F1 Score is the harmonic mean of precision and recall, making it ideal for imbalanced datasets where accuracy is misleading. With 95% accuracy but only 10% recall, the model is simply predicting the majority class (negative) almost always, so F1 Score captures the trade-off between false positives and false negatives better than accuracy or AUC-ROC.
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.
RMSE
Why it's wrong here
RMSE is for regression tasks.
F1 Score
Why this is correct
F1 score considers both precision and recall.
Related concept
Read the scenario before looking for a memorised answer.
Accuracy
Why it's wrong here
Accuracy is not reliable for imbalanced data.
AUC-ROC
Why it's wrong here
While useful, F1 is more direct for imbalanced classes.
Common exam traps
AWS often tests the misconception that high accuracy always indicates good model performance, especially on imbalanced datasets, leading candidates to overlook metrics like F1 Score that account for class distribution.
Detailed technical explanation
The F1 Score is calculated as 2 * (precision * recall) / (precision + recall), and it penalizes extreme imbalances between precision and recall. In this scenario, with recall at 10%, precision would also be low if the model predicts positive rarely, making F1 Score a more honest reflection of poor positive-class performance. Real-world applications like fraud detection or medical diagnosis rely on F1 Score because missing a positive case (low recall) or falsely flagging many negatives (low precision) both have high costs.
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
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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 — The F1 Score is the harmonic mean of precision and recall, making it ideal for imbalanced datasets where accuracy is misleading. With 95% accuracy but only 10% recall, the model is simply predicting the majority class (negative) almost always, so F1 Score captures the trade-off between false positives and false negatives better than accuracy or AUC-ROC.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 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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