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AIF-C01 Practice Question: A machine learning practitioner is building a…
A machine learning practitioner is building a binary classifier for a medical diagnosis application. The cost of a false negative (missing a disease) is very high. Which evaluation metric should the team emphasize?
⚠ Common exam trap
AWS often tests the distinction between recall and precision in high-stakes scenarios, and the trap here is that candidates may choose F1 score thinking it balances both metrics, not realizing that when false negatives are the primary concern, recall should be the emphasized metric over a balanced measure.
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
Recall (sensitivity) measures the proportion of actual positives correctly identified, which is critical when the cost of false negatives is high, as in medical diagnosis where missing a disease could have severe consequences. By maximizing recall, the model minimizes false negatives, ensuring that most patients with the disease are detected, even at the expense of more false positives.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Accuracy
Why it's wrong here
Accuracy counts all correct predictions, so with imbalanced data a model predicting the majority class scores highly while missing most disease cases. It is tempting as the default metric, and it would be correct when classes are balanced and false positives and false negatives carry equal cost.
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F1 score
Why it's wrong here
The F1 score is the harmonic mean of precision and recall, so it balances both error types rather than prioritising false negatives. It is tempting when classes are imbalanced and both errors matter, but here recall must be maximised, and F1 would be dragged down by precision.
- ✓
Recall
Why this is correct
Recall measures the proportion of actual positives correctly identified, directly minimising false negatives. In medical diagnosis, where missing a disease carries severe consequences, maximising recall ensures fewer diseased patients are wrongly classified as healthy. Precision would instead penalise false positives, which matter less here than the constraint of avoiding missed diagnoses.
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Precision
Why it's wrong here
Precision measures the proportion of positive predictions that are correct, so it targets false positives and ignores false negatives entirely. It is tempting because it matters when false positives are costly, such as flagging healthy patients for unnecessary invasive tests, but here missed disease is the concern.
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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.