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 for fraud detection. The dataset is highly imbalanced with only 1% fraudulent transactions. The model currently achieves 99% accuracy but only catches 5% of actual fraud cases. Which metric should the data scientist focus on to better evaluate model performance?
Precision
Why wrong: Precision focuses on false positives, not false negatives.
Accuracy
Why wrong: Accuracy is high due to class imbalance but does not reflect poor fraud detection.
Root Mean Squared Error (RMSE)
Why wrong: RMSE is used for regression tasks, not classification.
Recall
Recall measures the ability to find all positive samples, which is crucial for fraud detection.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Recall
In fraud detection with highly imbalanced data (1% fraud), accuracy is misleading because a model can achieve 99% accuracy by simply predicting 'not fraud' for all transactions. Recall (true positive rate) measures the proportion of actual fraud cases correctly identified, which is critical when the cost of missing fraud is high. The model currently catches only 5% of fraud, so improving recall is the primary goal to reduce false negatives.
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.
Precision
Why it's wrong here
Precision focuses on false positives, not false negatives.
Accuracy
Why it's wrong here
Accuracy is high due to class imbalance but does not reflect poor fraud detection.
Root Mean Squared Error (RMSE)
Why it's wrong here
RMSE is used for regression tasks, not classification.
Recall
Why this is correct
Recall measures the ability to find all positive samples, which is crucial for fraud detection.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
AWS often tests the misconception that accuracy is always the best metric, but in imbalanced classification, recall or precision-recall curves are more informative, and candidates must recognize that high accuracy can mask poor minority class performance.
Detailed technical explanation
Recall is defined as TP / (TP + FN), where FN represents missed fraud cases. In imbalanced datasets, optimizing recall often requires adjusting the classification threshold (e.g., lowering the decision threshold from 0.5 to 0.3) or using techniques like oversampling (SMOTE) or cost-sensitive learning to penalize false negatives more heavily. Real-world fraud detection systems prioritize recall to minimize financial loss, even if it increases false positives (lower precision), which can be handled by subsequent manual review.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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: Recall — In fraud detection with highly imbalanced data (1% fraud), accuracy is misleading because a model can achieve 99% accuracy by simply predicting 'not fraud' for all transactions. Recall (true positive rate) measures the proportion of actual fraud cases correctly identified, which is critical when the cost of missing fraud is high. The model currently catches only 5% of fraud, so improving recall is the primary goal to reduce false negatives.
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 30, 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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