This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 company is using Amazon SageMaker to train a model. Which TWO metrics should be used to evaluate a binary classification model?
Accuracy
Why wrong: Accuracy can be misleading for imbalanced data.
Perplexity
Why wrong: Perplexity is for language models.
AUC
AUC is a standard metric for binary classification.
F1 score
F1 score balances precision and recall.
Mean Absolute Error
Why wrong: MAE is for regression.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
AUC
AUC (Area Under the ROC Curve) is a threshold-independent metric that measures the model's ability to distinguish between positive and negative classes across all classification thresholds. For binary classification in SageMaker, AUC is robust to class imbalance and provides a single scalar value representing overall model performance, making it a standard evaluation metric.
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.
Accuracy
Why it's wrong here
Accuracy can be misleading for imbalanced data.
Perplexity
Why it's wrong here
Perplexity is for language models.
AUC
Why this is correct
AUC is a standard metric for binary classification.
Related concept
Read the scenario before looking for a memorised answer.
F1 score
Why this is correct
F1 score balances precision and recall.
Related concept
Read the scenario before looking for a memorised answer.
Mean Absolute Error
Why it's wrong here
MAE is for regression.
Common exam traps
The trap here is that candidates often pick Accuracy (A) as a default metric without considering class imbalance, or confuse regression metrics like MAE (E) with classification evaluation, while perplexity (B) is a distractor from NLP contexts.
Detailed technical explanation
AUC computes the area under the Receiver Operating Characteristic curve, which plots the True Positive Rate (TPR) against the False Positive Rate (FPR) at various threshold settings. In SageMaker, you can compute AUC using built-in algorithms like XGBoost or by specifying 'binary_classification' objective and using the 'auc' metric in the training job. A real-world scenario where AUC is critical is fraud detection, where the dataset is highly imbalanced (e.g., 99.9% legitimate transactions) and accuracy would be near 100% even if the model never catches fraud, whereas AUC captures the trade-off between catching fraud and 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: AUC — AUC (Area Under the ROC Curve) is a threshold-independent metric that measures the model's ability to distinguish between positive and negative classes across all classification thresholds. For binary classification in SageMaker, AUC is robust to class imbalance and provides a single scalar value representing overall model performance, making it a standard evaluation metric.
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 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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