Quick Answer
The answer is 0.8. Recall is calculated by dividing the number of true positives by the sum of true positives and false negatives, which in this case is 72 divided by 72 plus 18, yielding 72 over 90, or 0.8. This metric measures the model’s ability to capture all actual positive instances, making it critical when missing a positive case carries a high cost. On the CompTIA AI+ AI0-001 exam, you will often see a confusion matrix with four numbers and be asked to compute recall, precision, or accuracy; a common trap is confusing recall with precision, which uses false positives instead of false negatives. To remember the formula, think of recall as “how many of the real positives did we recall?”—the denominator is all actual positives (TP + FN). A helpful mnemonic is “Recall = Real positives Recovered,” where the denominator includes the false negatives that were missed.
This AI0-001 practice question tests your understanding of ai models and data engineering. 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.
The following is a confusion matrix for a binary classifier:
Predicted: Positive Predicted: Negative
Actual Positive: 80 20
Actual Negative: 30 70Refer to the exhibit. What is the recall of the model?
The following is a confusion matrix for a binary classifier:
Predicted: Positive Predicted: Negative
Actual Positive: 80 20
Actual Negative: 30 700.72
Why wrong: 0.72 is approximate accuracy (150/200=0.75, not 0.72).
0.8
TP=80, FN=20, recall=80/100=0.8
0.7
Why wrong: 0.7 is specificity (70/100=0.7).
0.73
Why wrong: 0.73 is approximate precision (80/(80+30)=0.727).
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
0.8
Recall is calculated as True Positives divided by (True Positives + False Negatives). From the confusion matrix, True Positives = 72 and False Negatives = 18, so recall = 72 / (72 + 18) = 72 / 90 = 0.8. This measures the model's ability to correctly identify all actual positive cases.
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.
0.72
Why it's wrong here
0.72 is approximate accuracy (150/200=0.75, not 0.72).
0.8
Why this is correct
TP=80, FN=20, recall=80/100=0.8
Related concept
Read the scenario before looking for a memorised answer.
0.7
Why it's wrong here
0.7 is specificity (70/100=0.7).
0.73
Why it's wrong here
0.73 is approximate precision (80/(80+30)=0.727).
Common exam traps
CompTIA often tests recall by providing a confusion matrix and expects candidates to correctly identify the denominator as TP+FN, not total samples, to avoid confusing recall with accuracy or precision.
Detailed technical explanation
Recall, also known as sensitivity or true positive rate, is critical in scenarios where missing a positive case is costly, such as fraud detection or medical diagnosis. The formula TP/(TP+FN) focuses on false negatives, which are the positive instances the model failed to capture. In this confusion matrix, the 18 false negatives represent actual positive cases that were incorrectly predicted as negative, directly reducing 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
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FAQ
AI Models and Data Engineering — This question tests AI Models and Data Engineering — Read the scenario before looking for a memorised answer..
The correct answer is: 0.8 — Recall is calculated as True Positives divided by (True Positives + False Negatives). From the confusion matrix, True Positives = 72 and False Negatives = 18, so recall = 72 / (72 + 18) = 72 / 90 = 0.8. This measures the model's ability to correctly identify all actual positive cases.
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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