mediumMultiple Choice
MLA-C01 Practice Question: A team is evaluating classification models for a…
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?
⚠ Common exam trap
Test-takers frequently default to F1 score as a 'balanced' metric, forgetting that when costs are asymmetric, the metric must reflect the specific business or clinical cost structure, not a generic harmonic mean.
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 directly minimizes false negatives. In medical diagnosis, missing a disease (false negative) is far more costly than a false alarm, so optimizing recall ensures the model captures as many true positive cases as possible.
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 positives correctly identified, so optimising it directly reduces false negatives. In medical diagnosis, where missing a condition is costlier than a false alarm, recall is the metric that satisfies the stem's asymmetric cost constraint.
- ✗
Accuracy
Why it's wrong here
Accuracy aggregates all errors equally, so a model can score highly while still missing the costly false negatives the scenario penalises. It is tempting because accuracy is the default headline metric for balanced datasets, where class costs are symmetric and overall correctness is genuinely what matters.
- ✗
F1 score
Why it's wrong here
F1 score balances precision and recall symmetrically, so it weights false positives and false negatives equally rather than prioritising the expensive misses. It is tempting because F1 is the standard choice for imbalanced classes, where both error types carry comparable cost.
- ✗
Precision
Why it's wrong here
Precision measures the proportion of predicted positives that are truly positive, so it penalises false positives rather than false negatives; optimising it can actively increase missed diagnoses, which is the costly error here. It is tempting because precision is the natural metric when false positives carry the greater cost, such as flagging healthy patients for unnecessary invasive follow-up tests.
Go deeper
Related to this question
About these practice questions
One of 665 original MLA-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.