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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.