- A
F2 score (beta=2) to prioritize recall over precision.
F2 score puts more weight on recall, aligning with the higher cost of false negatives.
- B
Area under the ROC curve (AUC-ROC) to measure overall discrimination.
Why wrong: AUC-ROC does not directly reflect the cost-sensitive decision threshold.
- C
F1 score to balance precision and recall equally.
Why wrong: F1 gives equal weight, but false negatives are costlier than false positives.
- D
Precision to minimize false positives.
Why wrong: False positives are less costly than false negatives, so recall is more important.
Question 45 of 1,000
Choosing Evaluation Metrics for Imbalanced Data: F1, F2, Precision, Recall
This AI0-001 practice question tests your understanding of ai concepts and foundations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
An AI team is deploying a predictive maintenance model for industrial equipment. The model predicts failure within a 30-day window. The cost of a false positive is 10% of the cost of a false negative. Which evaluation metric should the team prioritize?
Quick Answer
The answer is the F2 score (beta=2), which prioritizes recall over precision. This is correct because when false negatives are ten times costlier than false positives, the evaluation metric must emphasize minimizing missed failures, and the F-beta formula with beta=2 weights recall four times higher than precision, directly aligning with the asymmetric cost structure. On the CompTIA AI+ AI0-001 exam, this scenario tests your understanding of cost-sensitive evaluation metrics for imbalanced data, where the common trap is choosing F1 (beta=1) or AUC-ROC, which ignore cost ratios. A reliable memory tip: remember that beta > 1 means you care more about catching failures (recall), so for a costlier false negative, "beta bigger, recall bigger."
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
✓
F2 score (beta=2) to prioritize recall over precision.
The F2 score (beta=2) weights recall four times more than precision, which is appropriate because a false negative (missing a failure) costs 10 times more than a false positive (unnecessary maintenance). Prioritizing recall ensures the model captures as many true failures as possible, minimizing the higher-cost error type.
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.
- ✓
F2 score (beta=2) to prioritize recall over precision.
Why this is correct
F2 score puts more weight on recall, aligning with the higher cost of false negatives.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Area under the ROC curve (AUC-ROC) to measure overall discrimination.
Why it's wrong here
AUC-ROC does not directly reflect the cost-sensitive decision threshold.
- ✗
F1 score to balance precision and recall equally.
Why it's wrong here
F1 gives equal weight, but false negatives are costlier than false positives.
- ✗
Precision to minimize false positives.
Why it's wrong here
False positives are less costly than false negatives, so recall is more important.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may default to F1 score as a 'balanced' metric without considering the asymmetric cost structure, or they may incorrectly think AUC-ROC captures cost-sensitive performance.
Detailed technical explanation
How to think about this question
The F-beta score generalizes the harmonic mean of precision and recall, where beta determines the weight of recall relative to precision. With beta=2, recall is considered 4 times more important than precision (since recall weight = beta^2). In predictive maintenance, the cost matrix can be directly mapped to the beta parameter: beta = sqrt(cost_FN / cost_FP) = sqrt(10/1) ≈ 3.16, but beta=2 is a common heuristic that still heavily favors recall. Real-world deployments often use cost-sensitive learning or threshold tuning to directly optimize the expected cost rather than a single metric.
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
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Concepts and Foundations — This question tests AI Concepts and Foundations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: F2 score (beta=2) to prioritize recall over precision. — The F2 score (beta=2) weights recall four times more than precision, which is appropriate because a false negative (missing a failure) costs 10 times more than a false positive (unnecessary maintenance). Prioritizing recall ensures the model captures as many true failures as possible, minimizing the higher-cost error type.
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 →
Same concept, more angles
2 more ways this is tested on AI0-001
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. An AI system is deployed to detect fraudulent transactions. The system flags 5% of transactions as fraudulent, but the actual fraud rate is 0.1%. The business sees many false positives and wants to reduce them without significantly increasing false negatives. Which metric should be prioritized for optimization?
hard- A.Recall
- ✓ B.F1 score
- C.Accuracy
- D.Precision
Why B: The F1 score balances precision and recall, making it ideal when false positives are costly but false negatives must not increase significantly. Optimizing precision alone would reduce false positives but could increase false negatives, while recall alone would not address the false positive problem. The F1 score ensures both metrics are jointly optimized, aligning with the business requirement.
Variation 2. An AI model for detecting fraudulent transactions has high precision but low recall. Which business impact is most likely?
medium- A.The model has no impact on fraud detection
- B.The model detects all fraudulent transactions
- ✓ C.Many fraudulent transactions go undetected
- D.Many legitimate transactions are flagged as fraud
Why C: High precision means that when the model flags a transaction as fraudulent, it is very likely correct. However, low recall indicates that the model misses a significant proportion of actual fraudulent transactions. Therefore, the most likely business impact is that many fraudulent transactions go undetected, leading to financial losses.
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Last reviewed: Jul 4, 2026
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