This MLA-C01 practice question tests your understanding of ml model development. 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 training a binary classification model using imbalanced data where the positive class is only 1% of the dataset. The scientist wants to maximize the recall for the positive class while maintaining reasonable precision. Which evaluation metric is most appropriate to tune during model selection?
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 ideal for imbalanced datasets where the positive class is only 1%. By tuning the F1 score, the data scientist directly balances the trade-off between maximizing recall (capturing true positives) and maintaining reasonable precision (avoiding false positives), which aligns with the stated goal.
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.
✗
Log loss
Why it's wrong here
Log loss measures probability calibration, not classification performance for the minority class directly.
✗
Area under the ROC curve (AUC)
Why it's wrong here
AUC measures rank ordering but does not directly optimize recall at a specific threshold.
✓
F1 score
Why this is correct
F1 score combines precision and recall, making it suitable for imbalanced classes when both matter.
Related concept
Read the scenario before looking for a memorised answer.
✗
Accuracy
Why it's wrong here
Accuracy can be high even if the model predicts all negatives, failing to capture the minority class.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that AUC-ROC is always the best metric for imbalanced data, but the trap here is that AUC-ROC can remain high even when the model fails to recall the minority class, whereas the F1 score directly penalizes poor recall.
Detailed technical explanation
How to think about this question
The F1 score is defined as 2 * (precision * recall) / (precision + recall), and it is particularly useful when the class distribution is skewed because it treats false positives and false negatives symmetrically. In practice, tuning the F1 score often involves adjusting the decision threshold from the default 0.5 to a value that better captures the minority class, such as using precision-recall curves to find the optimal threshold. For example, in fraud detection where positive cases are rare, a model optimized for F1 score can catch more fraudulent transactions while keeping false alarms manageable.
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 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
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.
ML Model Development — This question tests ML Model Development — 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 ideal for imbalanced datasets where the positive class is only 1%. By tuning the F1 score, the data scientist directly balances the trade-off between maximizing recall (capturing true positives) and maintaining reasonable precision (avoiding false positives), which aligns with the stated goal.
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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Question Discussion
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