- A
Precision
Precision measures how many of the predicted positive cases are actually positive.
- B
Recall
Recall measures how many of the actual positive cases were correctly identified.
- C
F1 score
F1 is the harmonic mean of precision and recall, providing a balanced measure.
- D
Accuracy
Why wrong: Accuracy is misleading in imbalanced datasets; a model predicting all class A would achieve 90% accuracy.
- E
Root Mean Squared Error (RMSE)
Why wrong: RMSE is for regression, not classification.
Question 142 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 data scientist is evaluating a binary classification model's performance. The model was trained on a dataset where 90% of samples belong to class A and 10% to class B. Which THREE metrics are most appropriate to evaluate the model's ability to correctly identify class B? (Choose three.)
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
✓
Precision
Precision is appropriate because it measures the proportion of correctly predicted class B instances among all instances predicted as class B, directly addressing false positives. In an imbalanced dataset with only 10% class B, precision helps evaluate how many of the model's positive predictions are actually correct, which is critical when the cost of false positives is high.
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 this is correct
Precision measures how many of the predicted positive cases are actually positive.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Recall
Why this is correct
Recall measures how many of the actual positive cases were correctly identified.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
F1 score
Why this is correct
F1 is the harmonic mean of precision and recall, providing a balanced measure.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Accuracy
Why it's wrong here
Accuracy is misleading in imbalanced datasets; a model predicting all class A would achieve 90% accuracy.
- ✗
Root Mean Squared Error (RMSE)
Why it's wrong here
RMSE is for regression, not classification.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the trap that candidates default to accuracy as a universal metric, failing to recognize its inadequacy for imbalanced datasets, and that RMSE is incorrectly applied to classification problems instead of regression.
Detailed technical explanation
How to think about this question
Precision is defined as TP/(TP+FP) and Recall as TP/(TP+FN), while the F1 score is the harmonic mean of precision and recall (2 * (Precision * Recall) / (Precision + Recall)). In imbalanced classification, these metrics focus on the minority class performance, unlike accuracy which can be inflated by majority class dominance. Real-world applications like fraud detection or medical diagnosis rely on these metrics to avoid missing rare positive cases or overwhelming analysts with false alarms.
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.
Quick reference
IPv4 Address Class Summary
| Class | First Octet Range | Default Mask | Networks | Hosts per Network |
|---|---|---|---|---|
| A | 1–126 | /8 (255.0.0.0) | 126 | 16,777,214 |
| B | 128–191 | /16 (255.255.0.0) | 16,384 | 65,534 |
| C | 192–223 | /24 (255.255.255.0) | 2,097,152 | 254 |
| D | 224–239 | N/A | Multicast groups | — |
| E | 240–255 | N/A | Reserved / experimental | — |
127.x.x.x is reserved for loopback. Modern networks use CIDR (classless) rather than classful addressing.
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: Precision — Precision is appropriate because it measures the proportion of correctly predicted class B instances among all instances predicted as class B, directly addressing false positives. In an imbalanced dataset with only 10% class B, precision helps evaluate how many of the model's positive predictions are actually correct, which is critical when the cost of false positives is high.
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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