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 a dataset where the positive class represents only 1% of the data. The model's accuracy is 99%, but the recall for the positive class is 0%. Which metric should the scientist use to evaluate the model's performance effectively?
Area under the ROC curve (ROC AUC)
Why wrong: ROC AUC can be overly optimistic for imbalanced data.
Area under the Precision-Recall curve (PR AUC)
PR AUC is robust to class imbalance.
Accuracy
Why wrong: Accuracy is misleading for imbalanced data.
F1 score
Why wrong: F1 would be 0 because recall is 0.
Answer choices
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 Precision-Recall curve (PR AUC)
In a highly imbalanced dataset where the positive class is only 1%, accuracy is misleading because a model can achieve 99% accuracy by simply predicting the negative class for all samples, resulting in 0% recall for the positive class. The Area under the Precision-Recall curve (PR AUC) is the correct metric because it focuses on the performance of the positive class by evaluating the trade-off between precision and recall, making it sensitive to changes in the minority class. Unlike ROC AUC, which can be overly optimistic in imbalanced settings due to the large number of true negatives, PR AUC provides a more realistic assessment of model performance for rare events.
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.
Area under the ROC curve (ROC AUC)
Why it's wrong here
ROC AUC can be overly optimistic for imbalanced data.
Area under the Precision-Recall curve (PR AUC)
Why this is correct
PR AUC is robust to class imbalance.
Related concept
Read the scenario before looking for a memorised answer.
Accuracy
Why it's wrong here
Accuracy is misleading for imbalanced data.
F1 score
Why it's wrong here
F1 would be 0 because recall is 0.
Common exam traps
The trap here is that candidates often choose ROC AUC (Option A) because it is a common default metric, but they fail to recognize that in severe class imbalance, ROC AUC can be artificially inflated by the dominance of true negatives, whereas PR AUC is the correct choice for evaluating minority class performance.
Detailed technical explanation
PR AUC summarizes the precision-recall trade-off across all classification thresholds, making it robust to class imbalance because it ignores true negatives entirely. Under the hood, PR AUC is computed by interpolating precision values at different recall levels, and in scikit-learn, it uses the average precision score which weights precision by the increase in recall from the previous threshold. In real-world scenarios like fraud detection or rare disease diagnosis, PR AUC is the standard metric because it directly reflects the model's ability to rank positive instances higher than negative ones, even when positives are extremely rare.
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: Area under the Precision-Recall curve (PR AUC) — In a highly imbalanced dataset where the positive class is only 1%, accuracy is misleading because a model can achieve 99% accuracy by simply predicting the negative class for all samples, resulting in 0% recall for the positive class. The Area under the Precision-Recall curve (PR AUC) is the correct metric because it focuses on the performance of the positive class by evaluating the trade-off between precision and recall, making it sensitive to changes in the minority class. Unlike ROC AUC, which can be overly optimistic in imbalanced settings due to the large number of true negatives, PR AUC provides a more realistic assessment of model performance for rare events.
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
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