MLA-C01 ML Model Development Practice Question
A financial services firm is training a fraud detection model using SageMaker. The dataset is highly imbalanced (0.1% fraudulent transactions). The model currently achieves 99.9% accuracy but only catches 5% of fraud cases. Which metric should the team prioritize to evaluate model performance?
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
✓
Recall
Recall (true positive rate) measures the proportion of actual positives correctly identified. For fraud detection, catching fraud is critical; accuracy is misleading due to class imbalance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Accuracy
Why it's wrong here
Accuracy is not suitable for imbalanced datasets; it can be high even if the model misses most fraud cases.
- ✗
Precision
Why it's wrong here
Precision measures false positives; while important, it doesn't capture the ability to find fraud.
- ✓
Recall
Why this is correct
Recall focuses on capturing positive cases, which is critical in fraud detection.
- ✗
F1-score
Why it's wrong here
F1-score balances precision and recall, but recall alone is more direct when the priority is catching fraud.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-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 MLA-C01 exam.