Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
A data scientist trains a binary classification model to predict loan defaults. The dataset contains 98% non-default cases and only 2% default cases. The model predicts 'non-default' for every instance, achieving 98% accuracy on the test set. Which metric would best reveal that the model fails to identify any actual defaults?
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
Best answer
Recall for the default class
Recall calculates the proportion of actual defaults that the model correctly identifies. Since no defaults are predicted, recall is 0, clearly exposing the failure.
B
Distractor review
Precision for the default class
Precision is the proportion of predicted defaults that are actual defaults. As no defaults are predicted, precision would be undefined, not as directly interpretable as recall.
C
Distractor review
F1 score for the default class
F1 score is the harmonic mean of precision and recall. It would be 0, but recall alone already shows the problem clearly.
D
Distractor review
Accuracy
Accuracy is high (98%) because most cases are non-default, but it hides the model's 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: Recall for the default class — Recall for the default class measures how many actual defaults were correctly predicted. A recall of 0% indicates the model found none of the defaults, despite high accuracy. Precision for the default class would be undefined (division by zero as no defaults predicted). F1 score would be 0, but recall directly shows the failure to capture positive cases. Accuracy is misleading due to class imbalance.
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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