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AI-900 Practice Question: The difference between 'precision' and 'recall'…
This AI-900 practice question tests your understanding of the difference between 'precision' and 'recall'…. 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.
What is the difference between 'precision' and 'recall' as model evaluation metrics?
Answer choices
Why each option matters
Good practice is not just finding the correct option. The wrong answers often show the exact trap the exam wants you to fall into.
A
Distractor review
Precision is the speed of prediction; recall is the model's memory usage
These terms describe computational resources — precision and recall are classification metrics measuring different types of accuracy.
B
Distractor review
Recall is higher than precision whenever the model has seen more training data
The relationship between precision and recall depends on the classification threshold and class balance, not just training data volume.
C
Distractor review
Precision and recall are both the same metric, just calculated on different datasets
Precision and recall are distinct metrics measuring different things — false positive rate vs. false negative rate respectively.
D
Best answer
Precision measures correctness of positive predictions; recall measures coverage of actual positives
Precision = TP/(TP+FP): how often positive predictions are right. Recall = TP/(TP+FN): how many true positives were found.
Common exam trap
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Technical deep dive
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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.
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Keep practising from the same exam bank, or move into a focused topic page if this question exposed a weak area.
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Question 2
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Question 3
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Question 4
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Question 5
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Question 6
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FAQ
Questions learners often ask
What does this AI-900 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Precision measures correctness of positive predictions; recall measures coverage of actual positives — Precision measures what fraction of positive predictions were actually correct: TP / (TP + FP). Recall measures what fraction of actual positives were correctly identified: TP / (TP + FN). High precision means few false alarms; high recall means few misses. The F1 score balances both. The right trade-off depends on the use case — medical screening prioritises recall (catching all cases) while spam detection prioritises precision (avoiding false positives).
What should I do if I get this AI-900 question wrong?
Identify which AI-900 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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