Quick Answer
The answer is the F1 score, along with the Area Under the Precision-Recall Curve (AUC-PR), as the two most appropriate evaluation metrics for imbalanced binary classification. The F1 score is the harmonic mean of precision and recall, making it robust when the positive class is rare because it penalizes models that simply predict the majority class for all instances, unlike accuracy which can be misleadingly high. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your ability to select metrics that avoid the "accuracy paradox"—where a model appears strong by always guessing the majority class. A common trap is choosing accuracy or ROC-AUC, which can overestimate performance on severely imbalanced data. For a memory tip, think "F1 for the few": when the positive class is the minority, F1 and AUC-PR focus on how well you catch those rare, critical cases.
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 TWO metrics are appropriate for evaluating a binary classification model trained on imbalanced data? (Select TWO.)
Log loss
Why wrong: Log loss is not specifically for imbalance.
F1 score
F1 balances precision and recall.
Accuracy
Why wrong: Accuracy is misleading for imbalanced data.
Precision-recall curve
PR curve is sensitive to imbalance.
ROC-AUC
Why wrong: ROC-AUC can be optimistic for imbalanced data.
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 appropriate for imbalanced binary classification because it balances precision and recall, making it robust when the positive class is rare. Unlike accuracy, it does not get inflated by a majority negative class, and it directly penalizes models that predict the majority class for all instances.
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.
Log loss
Why it's wrong here
Log loss is not specifically for imbalance.
F1 score
Why this is correct
F1 balances precision and recall.
Related concept
Read the scenario before looking for a memorised answer.
Accuracy
Why it's wrong here
Accuracy is misleading for imbalanced data.
Precision-recall curve
Why this is correct
PR curve is sensitive to imbalance.
Related concept
Read the scenario before looking for a memorised answer.
ROC-AUC
Why it's wrong here
ROC-AUC can be optimistic for imbalanced data.
Common exam traps
Cisco often tests the misconception that ROC-AUC is always the best metric for imbalanced data, but the trap here is that ROC-AUC can be misleadingly high when the positive class is rare, whereas precision-recall curve and F1 score better reflect model performance on the minority class.
Detailed technical explanation
The precision-recall curve directly plots precision against recall at various thresholds, focusing on the performance of the positive class, which is critical for imbalanced data. Under the hood, the area under the precision-recall curve (PR-AUC) is more sensitive to false positives than ROC-AUC, making it a better diagnostic when the positive class is rare. In a real-world fraud detection scenario with 99.9% legitimate transactions, a model with 99.9% accuracy could miss all fraud, but a precision-recall curve would reveal poor recall.
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 appropriate for imbalanced binary classification because it balances precision and recall, making it robust when the positive class is rare. Unlike accuracy, it does not get inflated by a majority negative class, and it directly penalizes models that predict the majority class for all instances.
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.
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 →
Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which TWO metrics are appropriate for evaluating a binary classification model when the cost of false negatives is high?
mediumWhy C: When false negatives are costly, we want to minimize them, so recall (true positive rate) is important. Precision is also important to avoid too many false positives. F1 score balances both, but recall directly measures false negatives. AUC-ROC is a general measure. Accuracy can be misleading. So recall and F1 score are appropriate. Options: A: Recall, B: F1 score (both correct). C: Precision, D: AUC-ROC, E: Accuracy are not the best choices in this context.
Variation 2. Which TWO metrics are appropriate for evaluating a binary classification model when the cost of false negatives is high? (Choose 2)
easyWhy A: Option A (Recall) and Option D (Specificity) are correct. Recall measures true positives, and specificity measures true negatives, both relevant when false negatives are costly. Precision (B) and F1 (C) are not directly focused on false negatives. Accuracy (E) is misleading if imbalanced.
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Last reviewed: Jun 24, 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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