AIF-C01 Guidelines for Responsible AI Practice Question
A retail company uses a recommendation system that occasionally suggests inappropriate products to minors. Which responsible AI practice should be applied?
⚠ Common exam trap
AWS often tests the misconception that more data or automation alone can solve fairness and safety issues, when in fact responsible AI requires explicit governance mechanisms like human oversight for high-stakes or vulnerable-user scenarios.
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
✓
Implement human review of flagged recommendations
The correct practice is to implement human review of flagged recommendations. This aligns with the responsible AI principle of accountability, where automated systems must have oversight mechanisms to catch and correct inappropriate outputs, especially when minors are involved. Human-in-the-loop (HITL) validation ensures that edge cases or subtle context (e.g., age-inappropriate product suggestions) are caught before they reach end users, rather than relying solely on automated filters or feedback loops.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement human review of flagged recommendations
Why this is correct
Human review of flagged recommendations directly satisfies the need to catch inappropriate outputs before minors see them. Automated filters alone cannot reliably judge context, so routing borderline cases to reviewers provides the oversight layer that mitigates harm, matching the responsible AI principle of accountability and safety in this retail scenario.
- ✗
Rely solely on user feedback to improve
Why it's wrong here
User feedback is reactive and sparse, so harmful recommendations reach minors before any signal arrives; it cannot enforce age-based filtering. It is tempting because feedback loops genuinely improve relevance and ranking quality over time, but they address preference accuracy, not safety constraints that must be applied before output.
- ✗
Disable the recommendation system entirely
Why it's wrong here
Disabling the system removes the business capability entirely rather than controlling the harmful output, and the company still needs recommendations. It is tempting because switching off a misbehaving component guarantees no further harm, but responsible AI practise favours mitigation through filtering, age gating or human review while retaining the service.
- ✗
Increase the volume of training data
Why it's wrong here
More training data does not remove inappropriate items from the candidate set; the model still scores and serves them. It is tempting because additional data improves generalisation and reduces bias in many cases, but this scenario requires an explicit guardrail, such as filtering or content moderation, applied at inference time.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.