- A
F1-score
F1-score combines precision and recall, giving a balanced measure that penalizes low recall.
- B
Accuracy
Why wrong: Accuracy is high for imbalanced data even if the model misses many frauds, so it is not suitable.
- C
Recall
Recall measures the fraction of frauds detected, which is critical when missing a fraud is costly.
- D
Precision
Why wrong: Precision focuses on false positives, not false negatives; it may be high even if recall is low.
- E
Mean absolute error
Why wrong: MAE is a regression metric, not suitable for classification.
Question 437 of 1,000
hardMultiple SelectObjective-mapped
MLA-C01 Practice Question: A machine learning engineer is evaluating a…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 binary classification model for detecting fraudulent transactions. The dataset is highly imbalanced, and the cost of false negatives (missing a fraud) is very high. Which two evaluation metrics should the engineer consider? (Choose two.)
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
F1-score
The F1-score is the harmonic mean of precision and recall, making it a robust metric for imbalanced datasets where both false positives and false negatives matter. In this fraud detection scenario, the high cost of false negatives means recall is critical, but precision must also be considered to avoid overwhelming investigators with false alarms. The F1-score balances these two concerns, providing a single metric that penalizes extreme values in either precision or recall.
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.
- ✓
F1-score
Why this is correct
F1-score combines precision and recall, giving a balanced measure that penalizes low recall.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Accuracy
Why it's wrong here
Accuracy is high for imbalanced data even if the model misses many frauds, so it is not suitable.
- ✓
Recall
Why this is correct
Recall measures the fraction of frauds detected, which is critical when missing a fraud is costly.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Precision
Why it's wrong here
Precision focuses on false positives, not false negatives; it may be high even if recall is low.
- ✗
Mean absolute error
Why it's wrong here
MAE is a regression metric, not suitable for classification.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that accuracy is always a good metric, but in imbalanced classification problems, accuracy is a trap because a naive model can achieve high accuracy by simply predicting the majority class.
Detailed technical explanation
How to think about this question
The F1-score is defined as 2 * (precision * recall) / (precision + recall), and it reaches its maximum value of 1 only when both precision and recall are perfect. In practice, when tuning a fraud detection model, the F1-score helps identify the threshold that minimizes the combined cost of false positives and false negatives, especially when the class distribution is skewed (e.g., 99.9% legitimate, 0.1% fraudulent). A common real-world pitfall is that optimizing solely for recall can degrade precision to unacceptable levels, causing too many false positives that waste investigative resources.
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.
TExam Day Tips
- 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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: F1-score — The F1-score is the harmonic mean of precision and recall, making it a robust metric for imbalanced datasets where both false positives and false negatives matter. In this fraud detection scenario, the high cost of false negatives means recall is critical, but precision must also be considered to avoid overwhelming investigators with false alarms. The F1-score balances these two concerns, providing a single metric that penalizes extreme values in either precision or recall.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This MLA-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 MLA-C01 exam.
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