AIF-C01 Guidelines for Responsible AI Practice Question
A financial services company uses Amazon Bedrock to power a customer-facing chatbot that answers questions about loan products. During testing, the team notices the model sometimes produces responses that sound confident but contain fabricated interest rates. The compliance team requires a mechanism to automatically detect when responses are not grounded in the company's approved product documentation. Which AWS capability should the team use to meet this requirement?
⚠ Common exam trap
The trap here is assuming any content-safety or monitoring service detects hallucinations, when only grounding-aware evaluation that compares output against supplied source text can flag ungrounded statements.
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
✓
Amazon Bedrock Guardrails with contextual grounding checks
The requirement is to detect responses not supported by approved documentation, which is precisely what contextual grounding checks in Amazon Bedrock Guardrails do by scoring whether a response is grounded in the supplied reference text. Sentiment analysis, data drift monitoring, and API audit logging all operate on different layers and cannot judge whether a generated interest rate is factually supported.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon SageMaker Model Monitor data drift detection
Why it's wrong here
Model Monitor compares statistical distributions of incoming request data against a baseline captured at training time to detect drift in features. It does not inspect generated natural-language text for factual grounding, so fabricated interest rates that are statistically similar to normal inputs would not be detected. This addresses input distribution shift, not output hallucination.
- ✗
Amazon Comprehend sentiment analysis on the model output
Why it's wrong here
Comprehend sentiment analysis returns whether text is positive, negative, neutral, or mixed. It has no knowledge of the company's approved loan documentation, so it cannot determine whether a stated interest rate is fabricated. Sentiment scoring would pass a confidently worded but false rate as neutral or positive, leaving the compliance gap entirely unresolved.
- ✓
Amazon Bedrock Guardrails with contextual grounding checks
Why this is correct
Contextual grounding checks in Amazon Bedrock Guardrails evaluate whether a model response is supported by the source content provided in the prompt, and assign grounding and relevance scores. Because the team needs to detect fabricated rates that are not grounded in approved documentation, this feature directly filters ungrounded responses and can block or flag them before the customer sees them.
- ✗
AWS CloudTrail management event logging on the Bedrock endpoint
Why it's wrong here
CloudTrail records API activity such as who invoked the model and when, which supports auditing but not content correctness. It captures metadata about the InvokeModel call, not the semantic accuracy of the returned text. Fabricated rates would still reach customers, so the logging trail alone does not satisfy the requirement to automatically detect ungrounded responses.
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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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