Question 590 of 754
AI0-001 AI Models and Data Engineering Practice Question
A data scientist is evaluating a binary classification model for fraud detection. The dataset is highly imbalanced (99% non-fraud, 1% fraud). Which TWO metrics are most appropriate for assessing model performance? (Choose two.)
⚠ Common exam trap
CompTIA often tests the misconception that AUC-ROC is always the best metric for imbalanced datasets, but the trap here is that AUC-ROC can be misleadingly high even when the model performs poorly on the minority class, whereas precision and recall directly address the class imbalance.
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
✓
Precision
Precision is appropriate because it measures the proportion of predicted fraud cases that are actually fraudulent, which is critical when false positives (flagging legitimate transactions as fraud) are costly. In a highly imbalanced dataset like this (99% non-fraud), precision directly evaluates the model's ability to avoid overwhelming fraud analysts with false alarms.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Precision
Why this is correct
Precision measures the proportion of predicted fraud that is actually fraud, important to avoid false positives.
- ✓
Recall
Why this is correct
Recall measures the proportion of actual fraud that is detected, critical for catching fraud.
- ✗
F1 score
Why it's wrong here
F1 is useful but the question asks for two; precision and recall are more direct.
- ✗
Area under the ROC curve (AUC-ROC)
Why it's wrong here
AUC-ROC is less informative for imbalanced datasets; precision-recall is preferred.
- ✗
Accuracy
Why it's wrong here
Accuracy is high even if the model predicts all non-fraud, so it's misleading.
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Last reviewed: Jun 30, 2026
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