hardMultiple Choice
AIF-C01 Practice Question: A team trained a binary classifier to detect…
A team trained a binary classifier to detect fraudulent transactions. The dataset is highly imbalanced (1% fraud). The model achieves 99% accuracy but only catches 5% of actual fraud cases. Which metric should the team primarily optimize?
⚠ Common exam trap
In AWS AI Practitioner exams, note that accuracy can be misleading in imbalanced datasets, and recall is often the key metric for fraud detection because missing a fraud (false negative) is far more costly than a false alarm.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Recall
In fraud detection with 1% fraud rate, 99% accuracy is misleading because the model can achieve it by simply predicting 'not fraud' for all transactions. The model catches only 5% of actual fraud cases, meaning recall (true positive rate) is critically low at 5%. Optimizing recall directly increases the proportion of actual fraud cases correctly identified, which is the primary business requirement in fraud detection where missing a fraud (false negative) is far more costly than a false alarm.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Recall
Why this is correct
Recall measures the proportion of actual fraud cases detected. With only 5% caught, recall exposes the model's failure despite 99% accuracy, which is misleading under 1% class imbalance. Optimising recall directly targets catching more fraud.
- ✗
F1 score
Why it's wrong here
F1 score balances precision and recall, but with 1% fraud the team needs recall on the minority class, so optimising F1 still permits missing most fraud. F1 tempts as a balanced measure, yet recall, or precision-recall AUC, directly targets catching fraudulent cases.
- ✗
Accuracy
Why it's wrong here
Accuracy counts all correct predictions, so predicting "no fraud" everywhere yields 99% on this 1% fraud dataset while catching none; recall directly measures the 5% detection shortfall. Accuracy is valid only when classes are roughly balanced and error costs equal.
- ✗
Precision
Why it's wrong here
Precision measures how many predicted frauds are truly fraudulent, so a model flagging almost nothing can still score well while missing 95% of fraud; the scenario demands recall, which counts caught frauds. Precision suits spam filters, where false positives are the costly error.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.