- A
Area Under the ROC Curve (AUC)
AUC is robust to class imbalance and evaluates overall ranking performance.
- B
Recall
Why wrong: Recall alone does not consider false positives.
- C
Accuracy
Why wrong: Accuracy can be high even if the model ignores the positive class due to imbalance.
- D
Precision
Why wrong: Precision alone does not reflect how many positives are captured.
Question 510 of 1,755
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 data scientist is using Amazon SageMaker Autopilot to automatically build a binary classification model. After the Autopilot job completes, the best model has an accuracy of 0.85 on the validation set. However, the data scientist notices a class imbalance (90% negative, 10% positive). Which metric should the data scientist use to evaluate the model's performance on the positive class?
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
✓
Area Under the ROC Curve (AUC)
A is correct because Area Under the ROC Curve (AUC) is threshold-independent and evaluates the model's ability to distinguish between positive and negative classes across all classification thresholds. In the presence of severe class imbalance (90% negative, 10% positive), AUC provides a robust measure of model performance on the positive class without being skewed by the majority class, unlike accuracy which would be high even if the model predicts all negatives.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Area Under the ROC Curve (AUC)
Why this is correct
AUC is robust to class imbalance and evaluates overall ranking performance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Recall
Why it's wrong here
Recall alone does not consider false positives.
- ✗
Accuracy
Why it's wrong here
Accuracy can be high even if the model ignores the positive class due to imbalance.
- ✗
Precision
Why it's wrong here
Precision alone does not reflect how many positives are captured.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often default to accuracy as the primary metric, failing to recognize that class imbalance renders accuracy misleading, and they overlook AUC's threshold-agnostic property which is specifically designed for evaluating model performance on the minority class in imbalanced datasets.
Detailed technical explanation
How to think about this question
AUC is computed by plotting the True Positive Rate (Recall) against the False Positive Rate (1 - Specificity) at various threshold settings, and the area under this curve represents the probability that the model ranks a randomly chosen positive instance higher than a randomly chosen negative instance. In SageMaker Autopilot, the best model is selected based on the objective metric (e.g., AUC by default for binary classification), and AUC remains robust even when class distribution is skewed because it evaluates ranking quality rather than absolute classification counts. A real-world scenario is fraud detection where positive cases are rare (<1%), and AUC is preferred over accuracy to ensure the model can differentiate fraudulent transactions from legitimate ones.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Area Under the ROC Curve (AUC) — A is correct because Area Under the ROC Curve (AUC) is threshold-independent and evaluates the model's ability to distinguish between positive and negative classes across all classification thresholds. In the presence of severe class imbalance (90% negative, 10% positive), AUC provides a robust measure of model performance on the positive class without being skewed by the majority class, unlike accuracy which would be high even if the model predicts all negatives.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 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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