AIF-C01 Fundamentals of AI and ML Practice Question
A company is building a model to detect fraudulent transactions. The dataset has 1,000,000 transactions, of which only 1,000 are fraudulent. The team wants to evaluate the model's performance. Which metric is most appropriate to use as the primary evaluation metric?
⚠ Common exam trap
The trap here is choosing accuracy because it is commonly used, but it is deceptive when classes are heavily imbalanced.
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
✓
F1 score
For highly imbalanced datasets like fraud detection, accuracy is misleading. The F1 score balances precision and recall, making it suitable when both false positives and false negatives matter. It provides a single metric that reflects the model's ability to correctly identify fraud without being overwhelmed by the majority class.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
F1 score
Why this is correct
F1 score is the harmonic mean of precision and recall, providing a balance between the two. In fraud detection, both false positives and false negatives have costs, so a balanced metric is essential. F1 score is robust to class imbalance and is the most appropriate primary metric here.
- ✗
Recall
Why it's wrong here
Recall measures the proportion of actual positives that were correctly identified. It is crucial for fraud detection because missing fraud is costly. However, recall alone ignores false positives, which can also be expensive. A model that flags everything as fraud would have perfect recall but terrible precision. Thus, recall alone is not sufficient.
- ✗
Accuracy
Why it's wrong here
Accuracy measures the overall proportion of correct predictions. With such a severe class imbalance, a model that predicts 'not fraudulent' for every transaction would achieve 99.9% accuracy but fail to detect any fraud. Accuracy is misleading here and should not be used as the primary metric.
- ✗
Precision
Why it's wrong here
Precision measures the proportion of positive identifications that are actually correct. While important, it alone does not capture how many fraudulent transactions are missed. A model could have high precision by only flagging a few obvious frauds, but low recall. It is not the most comprehensive primary metric for this imbalanced problem.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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