Question 1,664 of 1,672
MLS-C01 Modeling Practice Question
A data scientist needs to evaluate a binary classification model. The dataset is balanced. Which metric is most appropriate to compare model performance?
⚠ Common exam trap
AWS often tests the misconception that F1 score or precision-recall metrics are always superior, but for balanced datasets, accuracy is the simplest and most appropriate metric, and candidates may overlook this by defaulting to imbalance-focused metrics.
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
✓
Accuracy
For a balanced binary classification dataset, accuracy is the most appropriate metric because it directly measures the proportion of correct predictions (true positives and true negatives) out of all predictions. Since the class distribution is equal, accuracy is not misleadingly high due to class imbalance, making it a reliable and straightforward measure of overall model performance.
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 it's wrong here
Recall is more useful when false negatives are costly.
- ✗
F1 score
Why it's wrong here
F1 is harmonic mean of precision and recall, good for imbalanced data.
- ✗
Precision
Why it's wrong here
Precision is more useful when false positives are costly.
- ✓
Accuracy
Why this is correct
For balanced classes, accuracy is a straightforward metric.
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Last reviewed: Jun 30, 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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