Quick Answer
The answer is the F1 score, because in highly imbalanced classification where 99% of instances are negative, a naive model can achieve 99% accuracy by simply predicting the majority class for every case, completely missing the rare positive class. The F1 score is the harmonic mean of precision and recall, making it the primary evaluation metric for imbalanced datasets since it penalizes models that sacrifice recall for precision or vice versa, directly reflecting how well the model identifies positive cases while minimizing false positives. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding that accuracy is misleading with class imbalance, and the F1 score is the go-to metric for binary classification problems where the positive class is rare—a common trap is choosing accuracy because it looks high. Remember the memory tip: when positives are scarce, F1 is the one that’s fair.
This MLS-C01 practice question tests your understanding of modeling. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 is training a binary classification model using Amazon SageMaker. The dataset is highly imbalanced (99% negative class, 1% positive class). The model currently achieves 99% accuracy but fails to detect most positive cases. Which metric should the data scientist primarily use to evaluate model performance?
ROC AUC
Why wrong: ROC AUC can be overly optimistic for imbalanced data.
F1 score
F1 score balances precision and recall, suitable for imbalanced data.
Recall
Why wrong: Recall alone ignores false positives.
Accuracy
Why wrong: Accuracy is misleading for imbalanced datasets.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
F1 score
In highly imbalanced datasets (99% negative, 1% positive), accuracy is misleading because a model can achieve 99% accuracy by simply predicting the majority class for all instances, failing to detect any positive cases. The F1 score (option B) is the harmonic mean of precision and recall, providing a balanced measure that penalizes models that trade off recall for precision or vice versa. This makes it the primary metric for evaluating binary classification performance on imbalanced data, as it directly reflects the model's ability to correctly identify positive cases while minimizing false positives.
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.
ROC AUC
Why it's wrong here
ROC AUC can be overly optimistic for imbalanced data.
F1 score
Why this is correct
F1 score balances precision and recall, suitable for imbalanced data.
Related concept
Read the scenario before looking for a memorised answer.
Recall
Why it's wrong here
Recall alone ignores false positives.
Accuracy
Why it's wrong here
Accuracy is misleading for imbalanced datasets.
Common exam traps
The trap here is that candidates see 99% accuracy and assume the model is performing well, failing to recognize that accuracy is meaningless on imbalanced datasets, and they may incorrectly choose ROC AUC because it is commonly used for binary classification without understanding its limitations with extreme class imbalance.
Detailed technical explanation
Under the hood, the F1 score is the harmonic mean of precision (TP/(TP+FP)) and recall (TP/(TP+FN)), which ensures that both false positives and false negatives are penalized equally. In Amazon SageMaker, when using built-in algorithms like XGBoost or Linear Learner, you can set the objective to 'binary:logistic' and monitor the F1 score via custom metrics or the validation metric parameter to guide hyperparameter tuning. A real-world scenario is fraud detection, where the positive class (fraud) is rare; optimizing for F1 ensures the model catches fraudulent transactions without overwhelming the team with false alarms.
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: F1 score — In highly imbalanced datasets (99% negative, 1% positive), accuracy is misleading because a model can achieve 99% accuracy by simply predicting the majority class for all instances, failing to detect any positive cases. The F1 score (option B) is the harmonic mean of precision and recall, providing a balanced measure that penalizes models that trade off recall for precision or vice versa. This makes it the primary metric for evaluating binary classification performance on imbalanced data, as it directly reflects the model's ability to correctly identify positive cases while minimizing false positives.
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 11, 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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