Question 1mediummultiple choice
Full question →mediummultiple choiceObjective-mapped
A manufacturer trains a model to detect defective parts on an assembly line. Only 2% of parts are defective. The model predicts 'non-defective' for all parts and achieves 98% accuracy. Which metric best reveals the model's inability to identify defective parts?
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
Accuracy
Accuracy is 98% in this case, which seems good but hides the model's failure to detect any defective parts.
B
Distractor review
Precision
Precision measures how many predicted defective parts are actually defective. Since no parts are predicted defective, precision is undefined or 0, but recall is a more direct indicator of missing positives.
C
Best answer
Recall
Recall (sensitivity) is the proportion of actual defective parts that the model correctly identifies. A recall of 0% clearly shows the model fails to detect any defects.
D
Distractor review
F1 Score
F1 Score is the harmonic mean of precision and recall. It would be 0, but recall directly reveals the issue without combining with precision.
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.
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
More questions from this exam
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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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: Recall — Recall (also called sensitivity or true positive rate) measures the proportion of actual defective parts that are correctly identified. Here, recall for the defective class is 0%, exposing the model's failure. Accuracy is high (98%) but misleading because it ignores the minority class. Precision would be undefined because no defective predictions are made. F1 Score would be 0, but recall directly shows the lack of positive detections.
What should I do if I get this AI-900 question wrong?
Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.
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