Question 9 of 1,672
MLS-C01 Modeling Practice Question
A data scientist needs to evaluate a binary classification model's performance. Which metric is most appropriate when the cost of false positives is very high?
⚠ Common exam trap
Many exam-takers confuse precision with recall or F1 score, mistakenly thinking that minimizing false positives is best achieved by maximizing recall or a balanced metric, rather than directly optimizing precision.
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
✓
Precision
Precision is the most appropriate metric when the cost of false positives is very high because it measures the proportion of positive identifications that were actually correct. In binary classification, precision = TP / (TP + FP), so a high precision means very few false positives occur, directly minimizing the costly error type.
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
Not cost-sensitive.
- ✗
F1 score
Why it's wrong here
Balance of precision and recall.
- ✗
Recall
Why it's wrong here
Minimizes false negatives.
- ✓
Precision
Why this is correct
Minimizes false positives.
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Last reviewed: Jun 24, 2026
This MLS-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 MLS-C01 exam.
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