Quick Answer
The answer is precision, as this metric directly measures the proportion of positive predictions that are correct, making it the ideal choice for assessing a model’s ability to avoid false positives. Precision is calculated as TP / (TP + FP), so with TP=1500 and FP=600, the result is 0.714, meaning 71.4% of predicted positives are true positives while 28.6% are false positives—exactly the inverse of the false positive rate. On the CompTIA AI+ AI0-001 exam, this question tests your ability to match a business requirement (avoiding false positives) to the correct classification metric, a common trap where students mistakenly choose recall or accuracy. A useful memory tip: think of precision as “picky precision”—it only cares about how many of your positive guesses were right, ignoring the negatives entirely.
This AI0-001 practice question tests your understanding of ai implementation and operations. 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 operations team monitors a classification model in production. The confusion matrix for the model shows the following values: TP=1500, FN=500, FP=600, TN=2400. Which metric should the team calculate to assess the model's ability to avoid false positives?
Accuracy
Why wrong: Accuracy = (TP+TN)/Total = 3900/5000 = 0.78, overall correctness.
F1-score
Why wrong: F1 = 2*(Precision*Recall)/(Precision+Recall), combines both but not specific to false positives.
Recall
Why wrong: Recall = TP/(TP+FN) = 1500/2000 = 0.75, measures false negatives, not false positives.
Precision
Precision = TP/(TP+FP) = 1500/2100 = 0.714, directly relates to 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 (TP / (TP + FP)) directly measures the proportion of positive identifications that were actually correct, making it the ideal metric to assess the model's ability to avoid false positives. With TP=1500 and FP=600, precision is 1500/(1500+600)=0.714, indicating that 71.4% of predicted positives are true positives, while 28.6% are false positives. The question explicitly asks about avoiding false positives, which is the inverse of precision's focus on the correctness of positive predictions.
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.
Accuracy
Why it's wrong here
Accuracy = (TP+TN)/Total = 3900/5000 = 0.78, overall correctness.
F1-score
Why it's wrong here
F1 = 2*(Precision*Recall)/(Precision+Recall), combines both but not specific to false positives.
Recall
Why it's wrong here
Recall = TP/(TP+FN) = 1500/2000 = 0.75, measures false negatives, not false positives.
Precision
Why this is correct
Precision = TP/(TP+FP) = 1500/2100 = 0.714, directly relates to false positives.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
CompTIA often tests the distinction between precision and recall by framing a question about 'avoiding false positives' or 'avoiding false negatives,' leading candidates to confuse precision with recall or to default to F1-score as a balanced metric.
Detailed technical explanation
Precision is also known as the positive predictive value (PPV) and is critical in scenarios where the cost of a false positive is high, such as spam detection (marking legitimate email as spam) or medical diagnosis (incorrectly diagnosing a disease). Under the hood, precision is sensitive to class imbalance; if the positive class is rare, even a small number of false positives can drastically lower precision. In production monitoring, precision helps teams tune thresholds to minimize costly false alarms while maintaining acceptable recall.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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
AI Implementation and Operations — This question tests AI Implementation and Operations — Read the scenario before looking for a memorised answer..
The correct answer is: Precision — Precision (TP / (TP + FP)) directly measures the proportion of positive identifications that were actually correct, making it the ideal metric to assess the model's ability to avoid false positives. With TP=1500 and FP=600, precision is 1500/(1500+600)=0.714, indicating that 71.4% of predicted positives are true positives, while 28.6% are false positives. The question explicitly asks about avoiding false positives, which is the inverse of precision's focus on the correctness of positive predictions.
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 30, 2026
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