Question 1mediummultiple choice
Full question →mediummultiple choiceObjective-mapped
A data scientist trains a binary classification model to predict whether a loan applicant will default (positive class) or not (negative class). The training data contains 5% default cases. The model predicts 'no default' for every applicant in the test set and achieves 95% accuracy. Which evaluation metric best reveals that the model is failing to identify any default cases?
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
A. Precision for the default class
Precision is the fraction of positive predictions that are correct. Since the model makes no positive predictions, precision is undefined (division by zero) and not helpful.
B
Best answer
B. Recall for the default class
Recall (sensitivity) for defaults is the fraction of actual defaults that the model correctly identifies. With no defaults predicted, recall = 0%, clearly showing the model's failure.
C
Distractor review
C. F1-score for the default class
F1-score is the harmonic mean of precision and recall. Since recall is 0, the F1-score is also 0, but recall alone already reveals the issue more directly.
D
Distractor review
D. Overall accuracy
Accuracy is 95% because the model correctly predicts the majority class (non-default) for all cases. This high value masks the complete failure to detect defaults.
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: B. Recall for the default class — Recall for the positive class (default) measures the proportion of actual default cases that the model correctly identifies. Since the model predicts 'no default' for all, recall for defaults is 0, revealing the failure. Accuracy is misleadingly high. Precision for defaults is undefined because no positive predictions are made. F1-score would also be 0, but recall directly shows the missed defaults.
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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