Quick Answer
The correct answer is Area under the Precision-Recall curve (AUPRC). This metric is ideal for highly imbalanced classification problems, such as fraud detection with only 0.1% positive cases, because it evaluates model performance solely on the minority class by balancing precision and recall, ignoring the overwhelming number of true negatives that can inflate ROC-AUC. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of how imbalanced datasets distort traditional metrics; a common trap is choosing ROC-AUC, which appears optimistic but fails when negatives dominate. Remember that AUPRC focuses on the rare event you care about, making it sensitive to false positives—critical when minimizing them is the goal. Memory tip: “PR for the rare” — Precision-Recall curves prioritize the positive class, so use AUPRC when the minority class is your main concern.
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 fraud detection model that must achieve low false positive rates. The dataset is highly imbalanced (0.1% positive class). Which metric is most appropriate for model evaluation?
RMSE
Why wrong: RMSE is a regression metric.
Accuracy
Why wrong: Accuracy is misleading for imbalanced data.
Area under the Precision-Recall curve
Best for imbalanced datasets.
R-squared
Why wrong: R-squared is a regression metric.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Area under the Precision-Recall curve
In highly imbalanced datasets (0.1% positive class), the Precision-Recall curve focuses on the performance of the positive class, which is the minority class of interest. Area under the Precision-Recall curve (AUPRC) is insensitive to the large number of true negatives, making it a robust metric for evaluating models where false positives must be minimized. Unlike ROC-AUC, which can be overly optimistic in severe imbalance, AUPRC directly reflects the trade-off between precision and recall for the rare positive class.
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 a regression metric.
Accuracy
Why it's wrong here
Accuracy is misleading for imbalanced data.
Area under the Precision-Recall curve
Why this is correct
Best for imbalanced datasets.
Related concept
Read the scenario before looking for a memorised answer.
R-squared
Why it's wrong here
R-squared is a regression metric.
Common exam traps
AWS often tests the misconception that ROC-AUC is always the best metric for imbalanced classification, but the trap here is that ROC-AUC can be overly optimistic because it considers true negatives, whereas Precision-Recall AUC focuses solely on the positive class and is the correct choice when false positives must be minimized.
Detailed technical explanation
The Precision-Recall curve plots precision (TP/(TP+FP)) against recall (TP/(TP+FN)) at various threshold settings, and the area under this curve summarizes the model's ability to rank positive instances higher than negative ones. In fraud detection, where the cost of false positives is high (e.g., blocking legitimate transactions), AUPRC directly penalizes models that generate many false positives, as precision drops sharply. A subtle behavior: AUPRC is not symmetric and its baseline is the proportion of positive class (0.1%), so a random model would have an AUPRC of 0.001, making it a strict evaluator for rare events.
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: Area under the Precision-Recall curve — In highly imbalanced datasets (0.1% positive class), the Precision-Recall curve focuses on the performance of the positive class, which is the minority class of interest. Area under the Precision-Recall curve (AUPRC) is insensitive to the large number of true negatives, making it a robust metric for evaluating models where false positives must be minimized. Unlike ROC-AUC, which can be overly optimistic in severe imbalance, AUPRC directly reflects the trade-off between precision and recall for the rare positive class.
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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