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AIF-C01 Practice Question: A data scientist is building a RAG application…
A data scientist is building a RAG application using Amazon Bedrock Knowledge Bases. The team requires that responses only use information from the uploaded documents and reject queries that are not related to the documents. Which Bedrock feature should be used to enforce this?
⚠ Common exam trap
The trap is that Knowledge Bases sounds like the answer because it is the RAG component, but the requirement is to enforce response restrictions and reject off-topic queries — that is Guardrails, not retrieval.
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
✓
Bedrock Guardrails
Amazon Bedrock Guardrails lets you define denied topics, content filters, and contextual grounding checks that constrain what a model can say. For a RAG app that must answer only from uploaded documents and reject off-topic queries, Guardrails' contextual grounding and denied-topics policies enforce that boundary at inference time. This is the Bedrock feature purpose-built for controlling model responses.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Bedrock Knowledge Bases
Why it's wrong here
Knowledge Bases handle ingestion, chunking, embedding and retrieval, but they do not reject off-topic queries; that requires a Guardrail with a denied-topics or grounding filter. It tempts because Knowledge Bases are the RAG component being used, and would be correct for connecting the model to the uploaded documents.
- ✗
Bedrock Agents
Why it's wrong here
Agents orchestrate multi-step tasks and call APIs, not restrict responses to retrieved documents or block unrelated queries. It tempts because Agents also integrate with Knowledge Bases, and would be correct when the application must reason over steps and invoke external actions to complete a workflow.
- ✗
Bedrock Model Evaluation
Why it's wrong here
Model Evaluation measures quality metrics such as accuracy, robustness and toxicity against datasets; it does not filter live queries or constrain answers to source documents. It tempts because evaluation verifies RAG quality, and would be correct when comparing models or retrieval configurations before deployment.
- ✓
Bedrock Guardrails
Why this is correct
Bedrock Guardrails apply contextual grounding and denied-topic filters that block responses unsupported by the retrieved source documents, forcing the model to reject out-of-scope queries. This enforces the requirement that answers derive only from the uploaded knowledge base content.
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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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