- A
The model has no false positives
Why wrong: AUC-ROC does not imply zero false positives.
- B
The model has excellent discriminative ability
AUC close to 1 indicates strong separation between classes.
- C
The model's performance is independent of the decision threshold
AUC-ROC aggregates performance over all thresholds, so it is threshold-independent.
- D
The model is well-calibrated
Why wrong: AUC does not measure calibration; it measures rank ordering.
- E
The model's accuracy is at least 95%
Why wrong: AUC does not directly translate to accuracy; accuracy depends on threshold.
Question 867 of 1,755
AUC Interpretation
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 data scientist is evaluating a binary classification model. The model's AUC-ROC is 0.95. Which TWO statements are true?
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
✓
The model has excellent discriminative ability
AUC-ROC measures the model's ability to distinguish between classes across all thresholds. A high AUC (close to 1) indicates good performance. AUC-ROC is threshold-independent. It does not directly indicate accuracy or calibration.
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.
- ✗
The model has no false positives
Why it's wrong here
AUC-ROC does not imply zero false positives.
- ✓
The model has excellent discriminative ability
Why this is correct
AUC close to 1 indicates strong separation between classes.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
The model's performance is independent of the decision threshold
Why this is correct
AUC-ROC aggregates performance over all thresholds, so it is threshold-independent.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The model is well-calibrated
Why it's wrong here
AUC does not measure calibration; it measures rank ordering.
- ✗
The model's accuracy is at least 95%
Why it's wrong here
AUC does not directly translate to accuracy; accuracy depends on threshold.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The model has excellent discriminative ability — AUC-ROC measures the model's ability to distinguish between classes across all thresholds. A high AUC (close to 1) indicates good performance. AUC-ROC is threshold-independent. It does not directly indicate accuracy or calibration.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is evaluating a binary classification model that predicts whether a customer will churn. The model achieves an AUC of 0.85 on the test set. Which TWO statements about AUC are correct? (Choose two.)
easy- ✓ A.AUC represents the probability that a randomly chosen positive instance is ranked higher than a randomly chosen negative instance.
- B.An AUC of 0.85 indicates the model is no better than random guessing.
- C.AUC is the average precision across all thresholds.
- D.AUC is equivalent to the accuracy of the model at the default threshold of 0.5.
- ✓ E.AUC is threshold-independent, meaning it evaluates the model's ranking performance across all thresholds.
Why A: AUC measures the probability that a randomly chosen positive instance is ranked higher than a randomly chosen negative instance, which is correctly stated in option A. AUC is also threshold-independent, evaluating model ranking across all thresholds, as stated in option E. Option B is incorrect because an AUC of 0.85 is better than random (0.5). Option C is incorrect because AUC is not average precision; average precision is a different metric. Option D is incorrect because AUC is not equivalent to accuracy at any specific threshold.
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Last reviewed: Jun 20, 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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