Quick Answer
The answer is accuracy. For a balanced binary classification dataset, accuracy is the most appropriate metric because it directly measures the proportion of correct predictions—both true positives and true negatives—out of all predictions. Since the class distribution is equal, accuracy is not inflated by a majority class, making it a reliable and straightforward measure of overall model performance. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of how class balance dictates metric choice; a common trap is defaulting to precision or recall, which are more useful for imbalanced datasets. Remember the memory tip: “Balanced data? Accuracy is your friend—it counts all correct guesses equally.”
This MLS-C01 practice question tests your understanding of modeling. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 data scientist needs to evaluate a binary classification model. The dataset is balanced. Which metric is most appropriate to compare model performance?
Recall
Why wrong: Recall is more useful when false negatives are costly.
F1 score
Why wrong: F1 is harmonic mean of precision and recall, good for imbalanced data.
Precision
Why wrong: Precision is more useful when false positives are costly.
Accuracy
For balanced classes, accuracy is a straightforward metric.
Answer choices
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.
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 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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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.
Detailed technical explanation
Accuracy is defined as (TP + TN) / (TP + TN + FP + FN) and provides a single global view of model correctness. In balanced datasets, the baseline accuracy is 50%, so any improvement above that is meaningful; however, in imbalanced datasets, accuracy can be inflated by predicting the majority class, which is why metrics like F1 or precision-recall curves are preferred. Real-world scenarios like medical diagnosis with balanced cohorts often use accuracy for initial model comparison before diving into cost-sensitive metrics.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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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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