Quick Answer
The answer is precision. This metric is the correct choice when false positives are costly because it specifically measures the proportion of true positive predictions out of all positive predictions made by the model, calculated as TP divided by TP plus FP. A high precision value means that when the model flags a result as positive, it is very likely correct, thereby minimizing costly false alarms. On the AWS Certified AI Practitioner AIF-C01 exam, this concept tests your ability to align evaluation metrics with business impact; a common trap is confusing precision with recall, which focuses on catching all actual positives regardless of false positives. To remember this, think of precision as “precision over panic”—you only want to act when you are precise, not when you are guessing. A simple mnemonic is “Precision: Positive Predictions that are Precise,” emphasizing that it penalizes every false positive.
This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. 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.
Which metric is most appropriate for evaluating a classification model when false positives are costly?
Precision
Precision is the fraction of true positives among predicted positives, addressing false positives.
F1 score
Why wrong: F1 balances precision and recall, but if false positives are the primary concern, precision alone is more direct.
Recall
Why wrong: Recall minimizes false negatives, not false positives.
Accuracy
Why wrong: Accuracy can be misleading with imbalanced classes and does not specifically address false positives.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Precision
Precision is the most appropriate metric when false positives are costly because it measures the proportion of true positive predictions among all positive predictions (TP / (TP + FP)). A high precision indicates that when the model predicts a positive class, it is very likely correct, minimizing the number of false positives. This directly aligns with the business requirement to avoid costly false alarms.
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.
Precision
Why this is correct
Precision is the fraction of true positives among predicted positives, addressing false positives.
Related concept
Read the scenario before looking for a memorised answer.
F1 score
Why it's wrong here
F1 balances precision and recall, but if false positives are the primary concern, precision alone is more direct.
Recall
Why it's wrong here
Recall minimizes false negatives, not false positives.
Accuracy
Why it's wrong here
Accuracy can be misleading with imbalanced classes and does not specifically address false positives.
Common exam traps
Cisco often tests the distinction between precision and recall by framing a cost scenario, and the trap here is that candidates confuse 'costly false positives' with 'costly false negatives' and incorrectly choose recall or F1 score without analyzing which error type is being penalized.
Detailed technical explanation
Precision is a conditional probability that directly quantifies the reliability of a positive prediction. In a real-world scenario like spam detection, a false positive (marking a legitimate email as spam) can cause user frustration and lost business, so maximizing precision ensures that flagged emails are almost certainly spam. The metric is computed from the confusion matrix and is sensitive to the decision threshold; lowering the threshold increases recall but typically decreases precision, creating a trade-off visualized in the precision-recall curve.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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FAQ
Fundamentals of AI and ML — This question tests Fundamentals of AI and ML — Read the scenario before looking for a memorised answer..
The correct answer is: Precision — Precision is the most appropriate metric when false positives are costly because it measures the proportion of true positive predictions among all positive predictions (TP / (TP + FP)). A high precision indicates that when the model predicts a positive class, it is very likely correct, minimizing the number of false positives. This directly aligns with the business requirement to avoid costly false alarms.
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.
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Last reviewed: Jun 25, 2026
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services 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 AIF-C01 exam.
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