- A
Precision
Why wrong: Precision alone does not account for false negatives; recall also matters.
- B
Micro-averaged F1 score
Why wrong: Micro-averaged F1 aggregates contributions from all labels and is dominated by frequent labels.
- C
Accuracy
Why wrong: Accuracy is misleading for imbalanced datasets because it can be high even if rare labels are always predicted wrong.
- D
Macro-averaged F1 score
Macro-averaged F1 computes F1 per label and averages equally, giving appropriate weight to rare labels.
Question 291 of 1,000
AIF-C01 AI and ML Fundamentals Practice Question
This AIF-C01 practice question tests your understanding of ai and ml fundamentals. 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 machine learning team is working on a multi-label classification problem. They have a highly imbalanced dataset where some labels appear very infrequently. Which evaluation metric is MOST appropriate for assessing model performance across all labels?
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
✓
Macro-averaged F1 score
Macro-averaged F1 score is the most appropriate metric for multi-label classification with highly imbalanced data because it computes the F1 score independently for each label and then averages them, giving equal weight to all labels regardless of their frequency. This ensures that the performance on rare labels is not overshadowed by the performance on frequent labels, which is critical when infrequent labels are equally important.
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.
- ✗
Precision
Why it's wrong here
Precision alone does not account for false negatives; recall also matters.
- ✗
Micro-averaged F1 score
Why it's wrong here
Micro-averaged F1 aggregates contributions from all labels and is dominated by frequent labels.
- ✗
Accuracy
Why it's wrong here
Accuracy is misleading for imbalanced datasets because it can be high even if rare labels are always predicted wrong.
- ✓
Macro-averaged F1 score
Why this is correct
Macro-averaged F1 computes F1 per label and averages equally, giving appropriate weight to rare labels.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between micro and macro averaging in imbalanced multi-label scenarios, where candidates mistakenly choose micro-averaged F1 because it is commonly used in multi-class problems, failing to recognize that it favors majority labels in multi-label settings.
Detailed technical explanation
How to think about this question
Macro-averaging computes the F1 score per label as 2 * (precision * recall) / (precision + recall) and then takes the arithmetic mean across all labels. This treats each label as equally important, so a model that performs well on rare labels but poorly on common ones can still achieve a high macro F1, which is desirable when the cost of missing a rare label is high. In contrast, micro-averaging aggregates the confusion matrix elements globally, effectively weighting each instance equally, which dilutes the impact of rare labels.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
AI and ML Fundamentals — This question tests AI and ML Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Macro-averaged F1 score — Macro-averaged F1 score is the most appropriate metric for multi-label classification with highly imbalanced data because it computes the F1 score independently for each label and then averages them, giving equal weight to all labels regardless of their frequency. This ensures that the performance on rare labels is not overshadowed by the performance on frequent labels, which is critical when infrequent labels are equally important.
What should I do if I get this AIF-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
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