- A
Precision
Why wrong: Precision focuses on false positives, but the main issue is missing actual frauds.
- B
F1-score
Why wrong: F1-score balances precision and recall, but recall is the primary concern.
- C
Recall
Correct: Recall measures the proportion of actual frauds that are correctly identified.
- D
AUC-ROC
Why wrong: AUC-ROC is a general measure but does not directly address the failure to detect frauds.
Question 222 of 1,000
Recall for Imbalanced Classification
This AI0-001 practice question tests your understanding of machine learning and deep learning. 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 company uses a neural network for fraud detection. The dataset has 99% legitimate, 1% fraudulent. The model achieves 99% accuracy but fails to detect most frauds. Which metric should they focus on?
Quick Answer
The answer is recall. In imbalanced classification, where legitimate transactions vastly outnumber fraudulent ones, a model can achieve high accuracy by simply predicting the majority class, but recall specifically measures the proportion of actual frauds correctly identified. This metric is critical because failing to detect fraud carries far greater cost than false alarms, and recall directly quantifies the model’s ability to capture those rare positive cases. On the CompTIA AI+ AI0-001 exam, this scenario tests your understanding of why accuracy is misleading for imbalanced datasets and how recall becomes the primary metric when missing positives is unacceptable. A common trap is choosing precision, which focuses on how many flagged cases are actually fraud, but recall prioritizes catching all frauds even at the expense of some false positives. For a quick memory tip: think of recall as “recalling all the bad guys from a lineup”—you want to catch every fraudster, not just be sure the ones you catch are guilty.
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 (sensitivity) measures the proportion of actual positives correctly identified. In this fraud detection scenario with 99% legitimate and 1% fraudulent transactions, a 99% accuracy can be achieved by simply predicting all transactions as legitimate, which yields 0% recall for the fraud class. Focusing on recall ensures the model captures the majority of fraudulent cases, addressing the critical failure to detect fraud despite high accuracy.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 it's wrong here
Precision focuses on false positives, but the main issue is missing actual frauds.
- ✗
F1-score
Why it's wrong here
F1-score balances precision and recall, but recall is the primary concern.
- ✓
Recall
Why this is correct
Correct: Recall measures the proportion of actual frauds that are correctly identified.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
AUC-ROC
Why it's wrong here
AUC-ROC is a general measure but does not directly address the failure to detect frauds.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that high accuracy implies good model performance, especially in imbalanced datasets, leading candidates to overlook recall as the critical metric for detecting rare events like fraud.
Detailed technical explanation
How to think about this question
In highly imbalanced datasets, accuracy is a misleading metric because a model can achieve high accuracy by always predicting the majority class. Recall specifically evaluates the model's performance on the minority class (fraud), calculated as TP/(TP+FN). Under the hood, optimizing recall may involve adjusting the decision threshold, using cost-sensitive learning, or resampling techniques; for example, in a neural network with a sigmoid output, lowering the classification threshold from 0.5 to 0.3 can increase recall at the expense of precision.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Recall — Recall (sensitivity) measures the proportion of actual positives correctly identified. In this fraud detection scenario with 99% legitimate and 1% fraudulent transactions, a 99% accuracy can be achieved by simply predicting all transactions as legitimate, which yields 0% recall for the fraud class. Focusing on recall ensures the model captures the majority of fraudulent cases, addressing the critical failure to detect fraud despite high accuracy.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 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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