- A
BLEU score
Why wrong: BLEU measures text generation quality, not classification performance.
- B
Accuracy
Why wrong: Accuracy can be misleading in imbalanced datasets; a model predicting all legitimate achieves 99.9% accuracy but fails to catch fraud.
- C
F1-score
F1-score balances precision and recall, making it robust for imbalanced classification tasks.
- D
Perplexity
Why wrong: Perplexity is used for language models, not for binary classification.
Question 360 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 data scientist is building a model to predict whether a transaction is fraudulent. The dataset has 99.9% legitimate transactions and 0.1% fraudulent ones. Which evaluation metric is MOST appropriate to assess model performance given this 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
✓
F1-score
With 99.9% legitimate transactions and only 0.1% fraudulent ones, accuracy would be misleadingly high (99.9%) even if the model never predicts fraud. The F1-score is the harmonic mean of precision and recall, making it robust to class imbalance by penalizing both false positives and false negatives. This makes it the most appropriate metric for evaluating fraud detection performance.
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.
- ✗
BLEU score
Why it's wrong here
BLEU measures text generation quality, not classification performance.
- ✗
Accuracy
Why it's wrong here
Accuracy can be misleading in imbalanced datasets; a model predicting all legitimate achieves 99.9% accuracy but fails to catch fraud.
- ✓
F1-score
Why this is correct
F1-score balances precision and recall, making it robust for imbalanced classification tasks.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Perplexity
Why it's wrong here
Perplexity is used for language models, not for binary classification.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the trap that candidates default to accuracy as the universal metric, failing to recognize that in extreme class imbalance (e.g., 99.9% vs 0.1%), accuracy becomes meaningless and F1-score is the standard alternative.
Detailed technical explanation
How to think about this question
The F1-score is calculated as 2 * (precision * recall) / (precision + recall), where precision measures the proportion of predicted frauds that are actual frauds, and recall measures the proportion of actual frauds correctly identified. In fraud detection, a low F1-score immediately reveals poor performance even if accuracy is high, because it requires both high precision (few false alarms) and high recall (catching most frauds). Real-world systems often use a precision-recall curve to select a threshold that balances these trade-offs.
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?
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: F1-score — With 99.9% legitimate transactions and only 0.1% fraudulent ones, accuracy would be misleadingly high (99.9%) even if the model never predicts fraud. The F1-score is the harmonic mean of precision and recall, making it robust to class imbalance by penalizing both false positives and false negatives. This makes it the most appropriate metric for evaluating fraud detection performance.
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
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 →
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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