AIF-C01 Guidelines for Responsible AI Practice Question
A retail bank is preparing to launch an Amazon Bedrock-based assistant that recommends credit card products to customers. The responsible AI review board requires the team to document how the system's outputs can be explained to regulators and customers. Which TWO actions best support explainability for this deployment? (Choose two.)
⚠ Common exam trap
The trap here is equating operational logging or output variety with explainability, when explainability specifically requires capturing and communicating the basis for a decision.
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
✓
Store the retrieved source documents and the model's cited references alongside each generated recommendation for later review
Explainability for a regulated recommendation assistant requires preserving the evidence behind each output and communicating the automated nature of the decision to affected customers. Retaining retrieved sources and citations makes reasoning reproducible during audits, while AI disclosure with a human-review path gives customers transparency and contestability. Logging and catalog changes do not expose reasoning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the model's temperature setting so recommendations show more variety across customers
Why it's wrong here
Temperature controls randomness in token sampling and has nothing to do with documenting or communicating reasoning. Raising it would make outputs less consistent and harder to justify to reviewers, potentially increasing unexplainable variation. This change would work against the goal of producing stable, defensible recommendations for a regulated financial product.
- ✗
Enable verbose AWS CloudTrail logging of every InvokeModel API call for the assistant
Why it's wrong here
CloudTrail records who called the API, when, and from where, which supports security auditing and accountability. It does not capture the semantic reasoning or the evidence behind a recommendation, so it cannot explain why a particular card was suggested. Operational logging is valuable but does not fulfill the explainability requirement on its own.
- ✓
Store the retrieved source documents and the model's cited references alongside each generated recommendation for later review
Why this is correct
Capturing the grounding documents and citations creates an auditable trail showing why a specific product was recommended. When a regulator or customer asks how a recommendation was produced, the team can reproduce the evidence the model used. This directly supports explainability because the reasoning basis is preserved rather than lost after inference, and it aligns with transparency expectations for consequential financial recommendations.
- ✗
Reduce the number of credit card products in the catalog so the model has fewer options to choose from
Why it's wrong here
Limiting the catalog might simplify outputs, but it does not make the model's decision process visible or justifiable. It also constrains legitimate business offerings without addressing documentation of reasoning. Explainability depends on capturing and communicating why a choice was made, not on shrinking the set of choices available to the model.
- ✓
Provide a customer-facing disclosure that the recommendation is generated by AI and how to request a human review
Why this is correct
Disclosing AI involvement and offering an escalation path makes the decision process transparent to the person affected. Customers understand the nature of the interaction and can contest or seek human judgment, which is a core explainability and contestability practice. For banking recommendations, this disclosure also helps satisfy consumer-protection expectations that automated advice be identifiable and reviewable.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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