MLS-C01 Modeling Practice Question
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?
⚠ Common exam trap
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.
Answer choices
Why each option matters
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.
Answer analysis
Option-by-option breakdown
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.
- ✗
R-squared
Why it's wrong here
R-squared is a regression metric.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.