easyMultiple ChoiceObjective-mapped
AIF-C01 Practice Question: A company that uses Amazon Bedrock for generating…
A company that uses Amazon Bedrock for generating product descriptions wants to ensure that the output does not contain any confidential information from its proprietary database that is used as context. The company uses a knowledge base in Bedrock to augment the model. The security team is concerned that the model might inadvertently regurgitate exact strings from the knowledge base. The company wants to adopt a solution that prevents this while still allowing the model to use the knowledge base for generating relevant descriptions. What should the company do?
⚠ Common exam trap
Candidates often confuse data-at-rest protection (encryption) with output filtering, or assume that limiting response length prevents data leakage, when in fact only a guardrail-based output filter can block exact string matches from the generated content.
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
✓
Configure Bedrock Guardrails with a 'Prompt/Response Output' filter to block responses that match exact phrases from the knowledge base.
Bedrock Guardrails can be configured with a 'Prompt/Response Output' filter that uses a deny list of exact phrases or patterns. This allows the model to use the knowledge base for context while blocking any generated responses that contain verbatim strings from the proprietary database, directly addressing the security team's concern about regurgitation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configure Bedrock Guardrails with a 'Prompt/Response Output' filter to block responses that match exact phrases from the knowledge base.
Why this is correct
Guardrails can filter out responses that contain specific strings, preventing regurgitation.
- ✗
Remove the knowledge base and rely solely on the model's pre-trained knowledge.
Why it's wrong here
The company wants to use the knowledge base for relevance; removing it would reduce quality.
- ✗
Reduce the maximum token limit for model responses so that it cannot generate long strings.
Why it's wrong here
Short responses could still contain exact matches; this is not a reliable prevention.
- ✗
Encrypt the knowledge base data using AWS KMS with a unique key.
Why it's wrong here
Encryption protects data at rest, but does not prevent the model from generating exact matches during inference.
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JA
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.