Quick Answer
The answer is recall, because when false negatives carry a significantly higher cost than false positives, the priority is to minimize missed positive cases, and recall directly measures the proportion of actual positives correctly identified. In medical diagnosis, failing to detect a disease can have severe consequences, so optimizing recall ensures the model captures as many true positives as possible, even at the expense of some false alarms. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of cost-sensitive metric selection—a common trap is choosing F1 score or accuracy, which treat errors equally or balance precision and recall, but recall is the correct choice when the cost of missing a positive is high. A useful memory tip: recall is about “catching all the real ones,” so when a missed diagnosis is dangerous, you want to recall every patient who is truly sick.
This MLA-C01 practice question tests your understanding of ml model development. 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 team is evaluating classification models for a medical diagnosis application. The cost of a false negative is much higher than the cost of a false positive. Which metric should be optimized during model selection?
Recall
Recall minimizes false negatives, directly addressing the high cost of missed diagnoses.
Accuracy
Why wrong: Accuracy treats false positives and false negatives equally, which is suboptimal when one class is more important.
F1 score
Why wrong: F1 balances precision and recall, but does not give extra weight to recall.
Precision
Why wrong: Precision reduces false positives, which is not the primary concern.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Recall
Option B is correct because recall (true positive rate) focuses on minimizing false negatives, which is the priority when a missed diagnosis is costly. Option A (precision) minimizes false positives. Option C (accuracy) treats all errors equally. Option D (F1 score) balances precision and recall but does not emphasize recall over precision.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Recall
Why this is correct
Recall minimizes false negatives, directly addressing the high cost of missed diagnoses.
Related concept
Read the scenario before looking for a memorised answer.
Accuracy
Why it's wrong here
Accuracy treats false positives and false negatives equally, which is suboptimal when one class is more important.
F1 score
Why it's wrong here
F1 balances precision and recall, but does not give extra weight to recall.
Precision
Why it's wrong here
Precision reduces false positives, which is not the primary concern.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
ML Model Development — study guide chapter
Learn the concepts, then practise the questions
ML Model Development practice questions
Targeted practice on this topic area only
All MLA-C01 questions
507 questions across all exam domains
AWS Certified Machine Learning Engineer Associate MLA-C01 study guide
Full concept coverage aligned to exam objectives
MLA-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
Practise MLA-C01 questions linked to ML Model Development.
Practise MLA-C01 questions linked to Deployment and Orchestration of ML Workflows.
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance and Security.
Practise MLA-C01 questions linked to MLA-C01 fundamentals.
Practise MLA-C01 questions linked to MLA-C01 scenario.
Practise MLA-C01 questions linked to MLA-C01 troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
The correct answer is: Recall — Option B is correct because recall (true positive rate) focuses on minimizing false negatives, which is the priority when a missed diagnosis is costly. Option A (precision) minimizes false positives. Option C (accuracy) treats all errors equally. Option D (F1 score) balances precision and recall but does not emphasize recall over precision.
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jun 23, 2026
This MLA-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 MLA-C01 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.