- A
F1 score
Why wrong: F1 balances precision and recall; if false negatives are the priority, recall alone should be optimized.
- B
Accuracy
Why wrong: Accuracy can be high due to majority class even if many positives are missed.
- C
Precision
Why wrong: Precision focuses on false positives, not false negatives.
- D
Recall
Recall = TP/(TP+FN). Maximizing recall minimizes false negatives.
Question 298 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.
A data scientist trains a binary classification model and obtains the following results on the test set: accuracy 0.92, precision 0.90, recall 0.85, F1 0.87. The dataset has 5% positive class. The business requirement is to minimize false negatives. Which metric should the team prioritize?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 (sensitivity) measures the proportion of actual positives correctly identified, which directly addresses the business requirement to minimize false negatives. With a 5% positive class, accuracy is misleadingly high because the model can simply predict the majority class (negative) and still achieve 95% accuracy, but this would result in zero recall. Prioritizing recall ensures the model captures as many true positives as possible, reducing false negatives at the cost of potentially more 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.
- ✗
F1 score
Why it's wrong here
F1 balances precision and recall; if false negatives are the priority, recall alone should be optimized.
- ✗
Accuracy
Why it's wrong here
Accuracy can be high due to majority class even if many positives are missed.
- ✗
Precision
Why it's wrong here
Precision focuses on false positives, not false negatives.
- ✓
Recall
Why this is correct
Recall = TP/(TP+FN). Maximizing recall minimizes false negatives.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
For the AWS AI Practitioner exam, recall is the key metric when minimizing false negatives is the business requirement. Accuracy can be misleading in imbalanced datasets like this one (5% positives). Candidates often pick accuracy out of habit, ignoring the specific business need.
Detailed technical explanation
How to think about this question
Recall is defined as TP/(TP+FN), where FN are false negatives; minimizing false negatives directly increases recall. In imbalanced datasets, threshold tuning (e.g., lowering the decision threshold) can improve recall but may reduce precision, creating a precision-recall tradeoff. The F1 score assumes equal importance of precision and recall, which is not the case here since the business prioritizes recall over precision.
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.
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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 (sensitivity) measures the proportion of actual positives correctly identified, which directly addresses the business requirement to minimize false negatives. With a 5% positive class, accuracy is misleadingly high because the model can simply predict the majority class (negative) and still achieve 95% accuracy, but this would result in zero recall. Prioritizing recall ensures the model captures as many true positives as possible, reducing false negatives at the cost of potentially more 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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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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