Quick Answer
The answer is the Area Under the Precision-Recall curve (AUPRC). This metric is the correct choice because with a 1:1000 class imbalance, the positive class is extremely rare, and AUPRC focuses exclusively on the minority class by evaluating the trade-off between precision and recall. Unlike ROC AUC, which can appear deceptively high when the majority class dominates, AUPRC is sensitive to changes in how well the model identifies the few positive instances, making it the most robust evaluation metric for imbalanced classification. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of when to avoid ROC AUC for skewed targets; a common trap is assuming ROC AUC works universally. Remember the memory tip: "When positives are scarce, PR curves are fair—ROC lies when negatives outnumber the prize."
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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 examining a dataset for a binary classification problem. The target variable has a 1:1000 imbalance. Which technique should be used to assess model performance during exploratory data analysis?
Area under the Precision-Recall curve
PR AUC is sensitive to class imbalance and focuses on the positive class.
F1 score
Why wrong: F1 score is threshold-dependent and may not capture overall performance across thresholds.
Area under the ROC curve
Why wrong: ROC AUC can be overly optimistic with extreme imbalance.
Cohen's kappa
Why wrong: Cohen's kappa accounts for chance but is less standard for imbalance.
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
With a 1:1000 class imbalance, the positive class is extremely rare. The Area Under the Precision-Recall curve (AUPRC) focuses on the performance of the positive class and is sensitive to changes in precision and recall, making it a robust metric for imbalanced datasets. Unlike ROC AUC, which can be overly optimistic when negatives dominate, AUPRC provides a realistic assessment of model performance on the minority class.
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 Precision-Recall curve
Why this is correct
PR AUC is sensitive to class imbalance and focuses on the positive class.
Related concept
Read the scenario before looking for a memorised answer.
F1 score
Why it's wrong here
F1 score is threshold-dependent and may not capture overall performance across thresholds.
Area under the ROC curve
Why it's wrong here
ROC AUC can be overly optimistic with extreme imbalance.
Cohen's kappa
Why it's wrong here
Cohen's kappa accounts for chance but is less standard for imbalance.
Common exam traps
The trap here is that candidates often default to ROC AUC as the universal metric for classification, not realizing that in extreme imbalance, ROC AUC can be misleadingly high because the false positive rate is diluted by the vast number of true negatives.
Detailed technical explanation
Precision-Recall curves are built by varying the decision threshold and plotting precision (TP/(TP+FP)) against recall (TP/(TP+FN)). The area under this curve is equivalent to the average precision, which is a weighted mean of precisions achieved at each threshold, with the increase in recall from the previous threshold used as the weight. In highly imbalanced datasets, the baseline for AUPRC is the proportion of positive samples (0.001), so any value above that indicates meaningful positive class detection.
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
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
The correct answer is: Area under the Precision-Recall curve — With a 1:1000 class imbalance, the positive class is extremely rare. The Area Under the Precision-Recall curve (AUPRC) focuses on the performance of the positive class and is sensitive to changes in precision and recall, making it a robust metric for imbalanced datasets. Unlike ROC AUC, which can be overly optimistic when negatives dominate, AUPRC provides a realistic assessment of model performance on the minority class.
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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Same concept, more angles
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 machine learning engineer trains a binary classifier on an imbalanced dataset where the positive class represents 1% of the data. After training, the model achieves 99% accuracy but only 10% recall on the positive class. Which metric should the engineer focus on to evaluate the model's performance on the minority class?
mediumWhy A: Option B is correct because the F1 score balances precision and recall, which is suitable for imbalanced datasets. Option A is wrong because accuracy can be misleading with imbalance. Option C is wrong because precision alone ignores recall. Option D is wrong because AUC-ROC may still be high even with poor recall.
Last reviewed: Jun 24, 2026
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