Question 1mediummultiple choice
Full question →mediummultiple choiceObjective-mapped
A data scientist has trained a binary classification model to detect fraudulent credit card transactions. The dataset contains 99.9% legitimate transactions and only 0.1% fraudulent ones. The model predicts all transactions as legitimate, achieving 99.9% accuracy on the test set. However, the business requires the model to actually catch as many fraudulent transactions as possible. Which metric would best reveal the model's failure to identify fraud?
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 high (99.9%) because the model correctly classifies almost all legitimate transactions. This metric hides the model's inability to detect fraud.
B
Best answer
Recall
Recall measures the fraction of actual fraudulent transactions that the model correctly identifies. Since the model never predicts fraud, recall is 0%, which clearly shows the failure.
C
Distractor review
Precision
Precision is the fraction of predicted positive cases that are truly positive. Because the model makes no positive predictions, precision is undefined or 0, but it does not directly indicate the missed fraud cases as clearly as recall.
D
Distractor review
F1 score
F1 score is the harmonic mean of precision and recall. While it would be 0, it combines both metrics. The more direct measure of the inability to catch fraud is recall.
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 positives that were correctly identified. In this case, recall would be 0% because no actual fraudulent transactions were caught. Accuracy is misleadingly high due to class imbalance. Precision would be undefined (no true positives) and F1 score would be 0, but recall directly shows the model cannot find fraud. The question asks for the metric that best reveals the failure to identify fraud, and recall is the most straightforward.
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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