Quick Answer
The correct answer is to tune the model to achieve an acceptable balance between recall and precision because responsible AI requires managing trade-offs between competing objectives to align with ethical principles and business needs. In fraud detection, high precision with low recall means the model is very accurate when it flags a transaction, but it misses many actual fraudulent cases, particularly among small transactions—leading to significant financial losses and unfair treatment of customers who are not protected. On the AWS Certified AI Practitioner AIF-C01 exam, this scenario tests your understanding of how precision and recall directly impact fairness and harm minimization, often appearing as a trap where you might overvalue precision at the expense of recall. A common memory tip is to think of recall as “catching all the bad guys” and precision as “not arresting innocent people”—for fairness in fraud detection, you need to catch enough bad guys (recall) even if it means a few more false alarms.
This AIF-C01 practice question tests your understanding of guidelines for responsible ai. 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 bank uses an AI system to detect fraudulent transactions. The model has high precision but low recall for small transactions, potentially missing fraud. Which approach aligns with responsible AI?
Send all flagged transactions to customers for confirmation
Why wrong: Customer fatigue may occur.
Focus only on precision to minimize false positives
Why wrong: Ignoring recall can miss fraud.
Tune the model to achieve an acceptable balance between recall and precision
Balancing metrics is a responsible approach.
Increase the detection threshold to reduce false positives
Why wrong: Higher threshold reduces recall more.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Tune the model to achieve an acceptable balance between recall and precision
Option C is correct because responsible AI requires balancing competing objectives like precision and recall to align with ethical principles and business needs. In fraud detection, high precision with low recall means many fraudulent transactions are missed, which can lead to significant financial losses and erode customer trust. Tuning the model to achieve an acceptable trade-off ensures that the system is both effective and fair, minimizing harm while maintaining operational viability.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Send all flagged transactions to customers for confirmation
Why it's wrong here
Customer fatigue may occur.
Focus only on precision to minimize false positives
Why it's wrong here
Ignoring recall can miss fraud.
Tune the model to achieve an acceptable balance between recall and precision
Why this is correct
Balancing metrics is a responsible approach.
Related concept
Read the scenario before looking for a memorised answer.
Increase the detection threshold to reduce false positives
Why it's wrong here
Higher threshold reduces recall more.
Common exam traps
Cisco often tests the misconception that increasing the detection threshold improves model performance overall, when in fact it only reduces false positives at the cost of lowering recall, which can be detrimental in high-stakes applications like fraud detection.
Detailed technical explanation
Precision and recall are inversely related through the decision threshold; lowering the threshold increases recall but decreases precision, and vice versa. In practice, the F1 score (harmonic mean of precision and recall) is often used to find the optimal threshold that balances both metrics. For a fraud detection model, the cost of false negatives (missed fraud) can be much higher than false positives, so the threshold should be tuned using a cost-sensitive approach or by analyzing the precision-recall curve to align with business risk tolerance.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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Guidelines for Responsible AI — study guide chapter
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FAQ
Guidelines for Responsible AI — This question tests Guidelines for Responsible AI — Read the scenario before looking for a memorised answer..
The correct answer is: Tune the model to achieve an acceptable balance between recall and precision — Option C is correct because responsible AI requires balancing competing objectives like precision and recall to align with ethical principles and business needs. In fraud detection, high precision with low recall means many fraudulent transactions are missed, which can lead to significant financial losses and erode customer trust. Tuning the model to achieve an acceptable trade-off ensures that the system is both effective and fair, minimizing harm while maintaining operational viability.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 25, 2026
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.
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