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 is training a binary classification model on imbalanced data (95% negative, 5% positive). Which metric is most appropriate for evaluating model performance?
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-ROC)
AUC-ROC is the most appropriate metric for imbalanced binary classification because it evaluates the model's ability to distinguish between positive and negative classes across all classification thresholds, without being biased by the 95% negative majority. It measures the trade-off between true positive rate and false positive rate, making it robust to class imbalance.
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.
✗
R-squared
Why it's wrong here
R-squared is for regression, not classification.
✗
Mean Squared Error (MSE)
Why it's wrong here
MSE is a regression metric, not suitable for classification.
✓
Area Under the ROC Curve (AUC-ROC)
Why this is correct
AUC-ROC measures the model's ability to distinguish between classes regardless of threshold, suitable for imbalanced data.
Related concept
Read the scenario before looking for a memorised answer.
✗
Accuracy
Why it's wrong here
Accuracy is misleading for imbalanced datasets, as a model that predicts all negatives would achieve 95% accuracy.
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, not realizing that with severe class imbalance, accuracy can be artificially high and completely mask poor performance on the minority class.
Detailed technical explanation
How to think about this question
AUC-ROC computes the area under the receiver operating characteristic curve, which plots the true positive rate (sensitivity) against the false positive rate (1-specificity) at various threshold settings. This metric is threshold-independent and remains informative even when the positive class is rare, as it evaluates ranking quality rather than absolute predictions. In practice, for a 95:5 imbalance, a model with AUC-ROC > 0.5 indicates better-than-random discrimination, whereas accuracy could be 95% from a trivial classifier.
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.
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: Area Under the ROC Curve (AUC-ROC) — AUC-ROC is the most appropriate metric for imbalanced binary classification because it evaluates the model's ability to distinguish between positive and negative classes across all classification thresholds, without being biased by the 95% negative majority. It measures the trade-off between true positive rate and false positive rate, making it robust to class imbalance.
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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Question Discussion
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