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 training a binary classification model on an imbalanced dataset (95% negative class, 5% positive class). The model achieves 95% accuracy but only predicts the negative class for all examples. Which metric should the scientist use to evaluate model performance more appropriately?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
AUC-ROC
AUC-ROC is robust to class imbalance because it evaluates the model's ability to discriminate between positive and negative classes across all classification thresholds, rather than relying on a single threshold. In this scenario, the model predicts only the negative class, so its true positive rate is 0 and false positive rate is 0, yielding an AUC-ROC of 0.5 (random performance), which correctly reflects the model's lack of predictive power.
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.
✗
F1 score
Why it's wrong here
F1 score is useful but may still be high if the model predicts only the majority class; AUC-ROC is more robust.
✗
Mean squared error
Why it's wrong here
MSE is for regression tasks, not classification.
✗
Accuracy
Why it's wrong here
Accuracy can be high even if the model always predicts the majority class, which is misleading.
✓
AUC-ROC
Why this is correct
AUC-ROC evaluates the model's ability to distinguish between classes regardless of threshold and is robust to imbalance.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose F1 score (Option A) thinking it handles imbalance well, but they forget that F1 score requires at least some true positives to be meaningful, and in this extreme case where the model predicts only negatives, F1 score collapses to 0 or undefined, whereas AUC-ROC correctly identifies random performance.
Detailed technical explanation
How to think about this question
AUC-ROC plots the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings, and the area under this curve quantifies the model's ability to rank positive instances higher than negative ones. In imbalanced datasets, AUC-ROC remains informative because it is independent of class prevalence, unlike accuracy which is biased toward the majority class. A real-world scenario is fraud detection, where fraudulent transactions are rare (e.g., 0.1%), and AUC-ROC helps compare models that prioritize ranking over threshold-based predictions.
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: AUC-ROC — AUC-ROC is robust to class imbalance because it evaluates the model's ability to discriminate between positive and negative classes across all classification thresholds, rather than relying on a single threshold. In this scenario, the model predicts only the negative class, so its true positive rate is 0 and false positive rate is 0, yielding an AUC-ROC of 0.5 (random performance), which correctly reflects the model's lack of predictive power.
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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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.
Question Discussion
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