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AI Concepts and TechniquesmediumMultiple ChoiceObjective-mapped

AI0-001 AI Concepts and Techniques Practice Question

A bank wants to detect fraudulent transactions in real-time. The dataset is highly imbalanced (99.9% legitimate, 0.1% fraud). Which evaluation metric is MOST appropriate for model performance?

⚠ Common exam trap

CompTIA often tests the misconception that accuracy is a reliable metric for imbalanced datasets, leading candidates to overlook that AUC-ROC or precision-recall curves are required when the minority class is extremely rare.

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

AUC-ROC

AUC-ROC is the most appropriate metric because it evaluates the model's ability to distinguish between the minority fraud class (0.1%) and the majority legitimate class across all classification thresholds, without being biased by the extreme class imbalance. Unlike accuracy, AUC-ROC remains robust when the dataset is 99.9% legitimate, as it measures the true positive rate against the false positive rate, providing a comprehensive view of model performance for rare event detection.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • AUC-ROC

    Why this is correct

    AUC-ROC is robust to imbalance and evaluates the model's ability to distinguish classes.

  • Accuracy

    Why it's wrong here

    Accuracy would be 99.9% by predicting all legitimate — misleading.

  • Recall

    Why it's wrong here

    Recall is important but does not capture false positives.

  • Precision

    Why it's wrong here

    Precision is important but does not capture recall trade-off.

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JA

Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.