This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. 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.
A team is evaluating a classification model. The confusion matrix shows: TP=80, FN=20, FP=10, TN=90. What is the precision?
0.89
Precision = 80/(80+10) = 0.8889 ≈ 0.89.
0.75
Why wrong: 0.75 is not a standard metric from these numbers.
0.80
Why wrong: 0.80 is recall (80/100), not precision.
0.90
Why wrong: 0.90 is accuracy ((80+90)/(80+20+10+90)), not precision.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
0.89
Precision is calculated as TP / (TP + FP). Here, TP=80 and FP=10, so precision = 80 / (80 + 10) = 80 / 90 = 0.888..., which rounds to 0.89. This metric measures the proportion of positive identifications that were actually correct.
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.
0.89
Why this is correct
Precision = 80/(80+10) = 0.8889 ≈ 0.89.
Related concept
Read the scenario before looking for a memorised answer.
0.75
Why it's wrong here
0.75 is not a standard metric from these numbers.
0.80
Why it's wrong here
0.80 is recall (80/100), not precision.
0.90
Why it's wrong here
0.90 is accuracy ((80+90)/(80+20+10+90)), not precision.
Common exam traps
Cisco often tests the distinction between precision and recall by providing confusion matrix values that make one metric easy to miscalculate if you confuse the denominator (TP+FP vs TP+FN).
Detailed technical explanation
Precision is a key metric when the cost of false positives is high, such as in spam detection where incorrectly flagging legitimate emails as spam (FP) is more damaging than missing some spam (FN). Under the hood, precision focuses on the classifier's output, answering: 'Of all the instances predicted as positive, how many were truly positive?' This contrasts with recall, which focuses on ground truth positives. In imbalanced datasets, precision can be misleadingly high if the model simply predicts the majority class, so it is often paired with recall via the F1-score.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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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: 0.89 — Precision is calculated as TP / (TP + FP). Here, TP=80 and FP=10, so precision = 80 / (80 + 10) = 80 / 90 = 0.888..., which rounds to 0.89. This metric measures the proportion of positive identifications that were actually correct.
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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