- A
Recall
Recall minimizes false negatives, capturing more actual positives, which matches the goal.
- B
Precision
Why wrong: Precision focuses on the proportion of positive predictions that are correct; it minimizes false positives, not what is asked.
- C
AUC-ROC
Why wrong: AUC-ROC measures the model's ability to distinguish between classes, not specifically recall.
- D
F1 score
Why wrong: F1 is the harmonic mean of precision and recall; it balances both, not solely maximizing recall.
Question 177 of 1,000
AIF-C01 AI and ML Fundamentals Practice Question
This AIF-C01 practice question tests your understanding of ai and ml fundamentals. 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.
During a binary classification project, the team wants to optimize for correctly identifying positive cases even if it means more false positives. Which metric should they maximize?
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
✓
Recall
Recall (also known as sensitivity or true positive rate) measures the proportion of actual positive cases that are correctly identified. By maximizing recall, the model minimizes false negatives, which aligns with the goal of catching as many true positives as possible, even at the cost of increasing false positives.
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.
- ✓
Recall
Why this is correct
Recall minimizes false negatives, capturing more actual positives, which matches the goal.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Precision
Why it's wrong here
Precision focuses on the proportion of positive predictions that are correct; it minimizes false positives, not what is asked.
- ✗
AUC-ROC
Why it's wrong here
AUC-ROC measures the model's ability to distinguish between classes, not specifically recall.
- ✗
F1 score
Why it's wrong here
F1 is the harmonic mean of precision and recall; it balances both, not solely maximizing recall.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The distinction between recall and precision is critical. By emphasizing catching all positives, recall is the correct metric; precision would be chosen if the scenario prioritized minimizing false positives.
Detailed technical explanation
How to think about this question
Recall is calculated as TP / (TP + FN), and maximizing it often involves lowering the decision threshold so that more instances are classified as positive. In practice, this trade-off is visualized in the precision-recall curve, where a high-recall model may have many false positives but captures nearly all true positives, which is critical in domains like fraud detection or disease screening where missing a positive case is costly.
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
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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: Recall — Recall (also known as sensitivity or true positive rate) measures the proportion of actual positive cases that are correctly identified. By maximizing recall, the model minimizes false negatives, which aligns with the goal of catching as many true positives as possible, even at the cost of increasing false positives.
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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