- A
Accuracy
Why wrong: Accuracy can be misleading if the disease is rare; a model could achieve high accuracy by always predicting negative.
- B
F1 score
Why wrong: F1 balances precision and recall; when recall is the priority, it should be the primary metric.
- C
Recall
Recall directly addresses false negatives by measuring how many actual disease cases are caught.
- D
Precision
Why wrong: Precision focuses on reducing false positives, not false negatives.
Question 57 of 1,000
AIF-C01 AI and ML Fundamentals Practice Question
This AIF-C01 practice question tests your understanding of ai and ml fundamentals. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Accuracy
Why it's wrong here
Accuracy can be misleading if the disease is rare; a model could achieve high accuracy by always predicting negative.
- ✗
F1 score
Why it's wrong here
F1 balances precision and recall; when recall is the priority, it should be the primary metric.
- ✓
Recall
Why this is correct
Recall directly addresses false negatives by measuring how many actual disease cases are caught.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Precision
Why it's wrong here
Precision focuses on reducing false positives, not false negatives.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Recall is defined as TP / (TP + FN), and optimizing for it often involves lowering the decision threshold of the classifier to increase sensitivity, which can be tuned using the ROC curve or precision-recall curve. In practice, for medical diagnosis, a model might be trained with a weighted loss function that penalizes false negatives more heavily, or use techniques like oversampling the minority class to improve recall without sacrificing too much precision.
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.
TExam Day Tips
- 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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
AI and ML Fundamentals — This question tests AI and ML Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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.
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