The answer is the Precision-Recall curve and the F-beta score with beta greater than 1. These two evaluation metrics for cost-sensitive fraud classification are correct because they directly emphasize recall over precision, which is essential when the cost of a false negative—missing a fraudulent transaction—is ten times higher than the cost of a false positive. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how to select metrics that align with asymmetric business costs, especially in imbalanced datasets where accuracy and ROC-AUC can be misleading. A common trap is choosing ROC-AUC, which remains optimistic even when recall is poor, or log loss, which does not directly reflect cost ratios. Remember the memory tip: when false negatives hurt more, you need metrics that “F-beta with recall in the door”—set beta > 1 to weight recall higher, and pair it with the Precision-Recall curve to see the trade-off clearly.
MLS-C01 Modeling Practice Question
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 machine learning engineer is evaluating a classification model that predicts whether a transaction is fraudulent. The model outputs a probability score. The cost of a false negative (missed fraud) is 10 times higher than the cost of a false positive (false alarm). Which TWO evaluation metrics should the engineer use to tune the model? (Choose TWO.)
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
Precision-Recall curve and F-beta score (with beta > 1) emphasize recall, which is important when false negatives are costly. Option B (ROC-AUC) is less sensitive to class imbalance. Option D (accuracy) is misleading for imbalanced data. Option E (log loss) is not directly tied to cost.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
F-beta score with beta = 2
Why this is correct
F-beta with beta > 1 weights recall higher than precision, matching the cost structure.
Related concept
Read the scenario before looking for a memorised answer.
✗
Accuracy
Why it's wrong here
Accuracy is misleading when classes are imbalanced.
✗
Log loss
Why it's wrong here
Log loss measures probability calibration, not directly tied to misclassification cost.
✗
ROC-AUC
Why it's wrong here
ROC-AUC can be optimistic for imbalanced datasets.
✓
Precision-Recall curve
Why this is correct
Precision-Recall curve focuses on the positive class, suitable for imbalanced data.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: F-beta score with beta = 2 — Precision-Recall curve and F-beta score (with beta > 1) emphasize recall, which is important when false negatives are costly. Option B (ROC-AUC) is less sensitive to class imbalance. Option D (accuracy) is misleading for imbalanced data. Option E (log loss) is not directly tied to cost.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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.
Question Discussion
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