Quick Answer
The answer is the F-beta score with beta=2. This is the most appropriate metric for cost-sensitive classification when false negatives are ten times more costly than false positives, because the F-beta score generalizes the F1 score by allowing recall to be weighted more heavily than precision; with beta=2, recall is considered four times as important as precision, directly reflecting the asymmetric cost structure. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how to select evaluation metrics based on business cost constraints, often appearing in scenario-based questions where you must map a cost ratio to the correct beta value. A common trap is choosing the F1 score (beta=1) or accuracy, which ignore cost imbalances. Memory tip: think of beta as the “cost multiplier” for false negatives—if false negatives are 10x more costly, beta=2 (since 2²=4) is a close approximation, and you can always round up to the nearest integer beta that squares to exceed the cost ratio.
This MLS-C01 practice question tests your understanding of modeling. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 building a binary classifier for loan default prediction. The cost of a false negative (missing a default) is 10 times higher than the cost of a false positive. Which evaluation metric is MOST appropriate?
Precision
Why wrong: Precision emphasizes false positives, not false negatives.
F-beta score with beta=2
F-beta with beta>1 gives more weight to recall, minimizing costly false negatives.
Accuracy
Why wrong: Accuracy does not account for cost differences.
Area under the ROC curve
Why wrong: ROC AUC measures overall separability, not cost-sensitive performance.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
F-beta score with beta=2
The F-beta score with beta=2 is the most appropriate metric because it weights recall (sensitivity) higher than precision, which is critical when false negatives are 10 times more costly than false positives. Beta=2 means recall is considered 2^2 = 4 times more important than precision, directly aligning with the asymmetric cost structure. This allows the model to be tuned to minimize missed defaults, even at the expense of more false alarms.
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 emphasizes false positives, not false negatives.
F-beta score with beta=2
Why this is correct
F-beta with beta>1 gives more weight to recall, minimizing costly false negatives.
Related concept
Read the scenario before looking for a memorised answer.
Accuracy
Why it's wrong here
Accuracy does not account for cost differences.
Area under the ROC curve
Why it's wrong here
ROC AUC measures overall separability, not cost-sensitive performance.
Common exam traps
The trap here is that candidates often default to AUC-ROC as a 'balanced' metric without realizing it does not incorporate asymmetric error costs, leading them to overlook the F-beta score which is explicitly designed for such scenarios.
Detailed technical explanation
The F-beta score generalizes the F1 score by allowing a tunable weight on recall via the beta parameter, where beta > 1 favors recall over precision. In practice, the cost ratio can be directly mapped to beta by setting beta = sqrt(cost_FN / cost_FP), so for a 10:1 cost ratio, beta ≈ 3.16, but beta=2 is a common conservative choice that still heavily emphasizes recall. This metric is particularly useful in credit risk modeling where the business cost of approving a bad loan (FN) far exceeds the opportunity cost of rejecting a good one (FP).
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: F-beta score with beta=2 — The F-beta score with beta=2 is the most appropriate metric because it weights recall (sensitivity) higher than precision, which is critical when false negatives are 10 times more costly than false positives. Beta=2 means recall is considered 2^2 = 4 times more important than precision, directly aligning with the asymmetric cost structure. This allows the model to be tuned to minimize missed defaults, even at the expense of more false alarms.
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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