- A
AUC-ROC
AUC-ROC is robust to imbalance and evaluates the model's ability to distinguish classes.
- B
Accuracy
Why wrong: Accuracy would be 99.9% by predicting all legitimate — misleading.
- C
Recall
Why wrong: Recall is important but does not capture false positives.
- D
Precision
Why wrong: Precision is important but does not capture recall trade-off.
Question 177 of 1,000
AI0-001 AI Concepts and Techniques Practice Question
This AI0-001 practice question tests your understanding of ai concepts and techniques. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 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?
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.
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.
- ✓
AUC-ROC
Why this is correct
AUC-ROC is robust to imbalance and evaluates the model's ability to distinguish classes.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco 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.
Detailed technical explanation
How to think about this question
AUC-ROC computes the area under the receiver operating characteristic curve, which plots the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. For imbalanced datasets, AUC-ROC is threshold-independent and reflects the model's ranking quality, meaning a high AUC-ROC (e.g., >0.95) indicates that the model assigns higher scores to fraud cases than to legitimate ones, even if the absolute probabilities are low. In real-time fraud detection, this allows the bank to tune the decision threshold based on cost-benefit analysis, balancing false positives (customer inconvenience) against false negatives (financial loss).
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.
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
Questions learners often ask
What does this AI0-001 question test?
AI Concepts and Techniques — This question tests AI Concepts and Techniques — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
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.
About these practice questions
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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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