Quick Answer
The answer is recall. In the context of imbalanced dataset metrics, recall—also known as sensitivity—measures how many actual positive cases (fraudulent transactions) the model correctly identifies, whereas accuracy can be misleading when the majority class dominates. Here, the model’s 99% accuracy simply reflects its ability to correctly classify the 99% legitimate transactions, while failing to catch most fraud means recall is near zero. On the CompTIA AI+ AI0-001 exam, this scenario tests your understanding that accuracy is a poor metric for imbalanced datasets; the common trap is to assume high accuracy equals good performance. A quick memory tip: “Recall catches the rare, rare catch—accuracy can’t match.”
This AI0-001 practice question tests your understanding of ai concepts and foundations. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data scientist is training a binary classification model to detect fraudulent transactions. The dataset has 99% legitimate transactions and 1% fraudulent. The model achieves 99% accuracy but fails to catch most fraud. Which metric should the team prioritize to evaluate model performance?
F1 score
Why wrong: F1 balances precision and recall, but recall alone is more appropriate when missing fraud is costly.
Precision
Why wrong: Precision focuses on false positives, not on catching fraud.
Accuracy
Why wrong: Accuracy is misleading on imbalanced datasets.
Recall
Recall measures the ability to catch fraudulent transactions, which is the primary goal.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Recall
Recall (sensitivity) measures the proportion of actual positive cases (fraud) correctly identified. With 99% accuracy but failing to catch most fraud, the model is biased toward the majority class (legitimate transactions), so recall is the critical metric to ensure fraud detection improves.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
F1 score
Why it's wrong here
F1 balances precision and recall, but recall alone is more appropriate when missing fraud is costly.
Precision
Why it's wrong here
Precision focuses on false positives, not on catching fraud.
Accuracy
Why it's wrong here
Accuracy is misleading on imbalanced datasets.
Recall
Why this is correct
Recall measures the ability to catch fraudulent transactions, which is the primary goal.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
CompTIA often tests the misconception that high accuracy implies good model performance, especially in imbalanced datasets, leading candidates to overlook recall as the appropriate metric for minority class detection.
Detailed technical explanation
In binary classification with severe class imbalance (e.g., 99:1), accuracy is a poor metric because a naive classifier that always predicts the majority class achieves 99% accuracy. Recall (TP/(TP+FN)) directly captures the model's sensitivity to the minority class (fraud). Real-world fraud detection systems often tune the decision threshold to maximize recall while accepting a higher false positive rate, then use precision-recall curves to balance costs.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
AI Concepts and Foundations — This question tests AI Concepts and Foundations — Read the scenario before looking for a memorised answer..
The correct answer is: Recall — Recall (sensitivity) measures the proportion of actual positive cases (fraud) correctly identified. With 99% accuracy but failing to catch most fraud, the model is biased toward the majority class (legitimate transactions), so recall is the critical metric to ensure fraud detection improves.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. When evaluating a binary classification model, which two metrics are most appropriate for imbalanced datasets? (Choose two.)
mediumWhy C: Recall (Option C) is correct because it measures the proportion of actual positive cases correctly identified, which is critical in imbalanced datasets where the minority class is of primary interest. Precision (Option E) is correct because it measures the accuracy of positive predictions, helping to avoid false positives when the positive class is rare. Together, recall and precision provide a balanced view of model performance on the minority class, unlike accuracy which can be misleadingly high by simply predicting the majority class.
Variation 2. A machine learning engineer wants to evaluate a binary classifier. Which metric is MOST appropriate when the positive class is rare (e.g., 1% of total data)?
easyWhy B: When the positive class is rare (e.g., 1% of total data), accuracy is misleading because a classifier that always predicts the negative class would achieve 99% accuracy. The F1-score is the harmonic mean of precision and recall, making it robust to class imbalance by focusing on the positive class performance. It is the most appropriate metric for evaluating binary classifiers on imbalanced datasets.
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Last reviewed: Jun 30, 2026
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.
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