Quick Answer
The answer is the precision-recall curve. In a highly imbalanced classification problem like fraud detection with only 0.1% positive cases, accuracy is a dangerous metric because a model that always predicts the majority class will achieve 99.9% accuracy without actually identifying any fraud. The precision-recall curve focuses exclusively on the positive class by plotting precision (how many predicted positives are correct) against recall (how many actual positives are caught) across different decision thresholds, making it robust to extreme class imbalance. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept frequently appears in scenario-based questions where accuracy is a trap—the exam tests your ability to recognize that a high accuracy score can be meaningless when the minority class is critical. A common memory tip: when the positive class is rare, think “precision-recall, not accuracy’s false call.”
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 company is building a binary classifier to detect fraudulent transactions. The dataset is highly imbalanced with only 0.1% positive cases. The data scientist uses logistic regression and obtains 99.9% accuracy on the test set. Which metric should the data scientist use to evaluate the model's performance?
ROC AUC
Why wrong: ROC AUC can be misleading for highly imbalanced data.
Precision-recall curve
Precision-recall curves focus on the positive class and handle imbalance well.
Precision
Why wrong: Precision alone ignores recall.
F1 score
Why wrong: F1 score is a single number that may hide performance issues.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Precision-recall curve
With only 0.1% positive cases, accuracy is misleading because a model that always predicts 'not fraudulent' achieves 99.9% accuracy. The precision-recall curve focuses on the positive class and is robust to extreme class imbalance, showing the trade-off between precision and recall across thresholds. This makes it the best choice for evaluating a binary classifier on highly imbalanced fraud detection data.
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.
ROC AUC
Why it's wrong here
ROC AUC can be misleading for highly imbalanced data.
Precision-recall curve
Why this is correct
Precision-recall curves focus on the positive class and handle imbalance well.
Related concept
Read the scenario before looking for a memorised answer.
Precision
Why it's wrong here
Precision alone ignores recall.
F1 score
Why it's wrong here
F1 score is a single number that may hide performance issues.
Common exam traps
The trap here is that candidates see 'ROC AUC' as a standard metric and forget that it can be inflated by a large number of true negatives in imbalanced datasets, making precision-recall the correct choice for evaluating rare event classifiers.
Detailed technical explanation
The precision-recall curve plots precision (TP/(TP+FP)) against recall (TP/(TP+FN)) for all possible classification thresholds, ignoring true negatives entirely. In fraud detection, where the cost of missing a fraud (false negative) is high, the area under the precision-recall curve (AUPRC) provides a more meaningful summary than ROC AUC because it focuses on the positive class. A real-world scenario is credit card fraud detection, where the model must rank transactions by fraud probability and the business sets a threshold based on acceptable precision-recall trade-offs.
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: Precision-recall curve — With only 0.1% positive cases, accuracy is misleading because a model that always predicts 'not fraudulent' achieves 99.9% accuracy. The precision-recall curve focuses on the positive class and is robust to extreme class imbalance, showing the trade-off between precision and recall across thresholds. This makes it the best choice for evaluating a binary classifier on highly imbalanced fraud detection data.
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
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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