Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
A data scientist trains a binary classification model to detect fraudulent transactions. The dataset contains only 2% fraudulent transactions. The model achieves 98% overall accuracy, but it fails to detect any fraudulent transactions, classifying all transactions as legitimate. Which metric would most clearly reveal this failure?
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
Precision would be low or undefined because the model predicts no positive cases, but recall more directly shows the inability to find fraud.
B
Best answer
Recall
Recall (true positive rate) is 0 when no fraudulent transactions are identified, exposing the model's failure.
C
Distractor review
F1 score
F1 score is the harmonic mean of precision and recall; it would be 0 but does not pinpoint the issue as clearly as recall alone.
D
Distractor review
Specificity
Specificity measures true negative rate, which would be high (98%) and thus hide the failure to detect fraud.
Common exam trap
Common exam trap: usable hosts are not the same as total addresses
Subnetting questions often tempt you into counting all addresses. In normal IPv4 subnets, the network and broadcast addresses are not usable host addresses.
Technical deep dive
How to think about this question
Subnetting questions test whether you can identify the network, broadcast address, usable range, mask and correct subnet. Slow down enough to calculate the block size correctly.
KKey Concepts to Remember
- CIDR notation defines the prefix length.
- Block size helps identify subnet boundaries.
- Network and broadcast addresses are not usable hosts in normal IPv4 subnets.
- The required host count determines the smallest suitable subnet.
TExam Day Tips
- Write the block size before choosing the subnet.
- Check whether the question asks for hosts, subnets or a specific address range.
- Do not confuse /24, /25, /26 and /27 host counts.
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?
CIDR notation defines the prefix length.
What is the correct answer to this question?
The correct answer is: Recall — Overall accuracy is misleading when classes are imbalanced. Recall (also called sensitivity or true positive rate) measures how many actual positive (fraudulent) cases were correctly identified. If the model fails to detect any fraudulent transactions, recall will be 0, clearly showing the problem. Precision would be undefined (0/0) or 0 if no fraud predicted. F1 score would be 0. Specificity measures true negatives and would be high (98%), masking the failure to detect fraud. Therefore, recall is the metric that best reveals the failure.
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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