mediumMultiple Choice
AIF-C01 Practice Question: A data scientist is using Amazon Bedrock to build…
A data scientist is using Amazon Bedrock to build a question-answering system over a large corpus of technical manuals. They want to ensure that the model's answers are grounded in the retrieved documents and that the model does not hallucinate. Which feature should they enable?
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 with grounding support
Bedrock Knowledge Bases provides source attribution, and when combined with model inference, the model can be instructed to answer only from the retrieved chunks. However, Bedrock Guardrails' grounding check specifically verifies that the model's response is supported by the retrieved context, reducing hallucination.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Embedding model with higher dimensionality
Why it's wrong here
Higher-dimensional embeddings change only the vector representation's granularity, not whether generated answers stay anchored to retrieved passages. They are genuinely useful when semantic discrimination between near-identical chunks is poor, but they leave the generation step unconstrained. Grounding requires retrieval-augmented generation with citations, which forces the model to answer from the supplied source documents.
- ✗
Larger chunk sizes in the knowledge base
Why it's wrong here
Chunk size affects retrieval granularity, not whether the model cites retrieved passages; hallucination is curbed by RetrieveAndGenerate with citations, which grounds responses in source text. Larger chunks suit improving context completeness for dense documents, not enforcing grounding.
- ✓
Bedrock Guardrails with grounding support
Why this is correct
Bedrock Guardrails with grounding support validates responses against the retrieved source documents, applying a grounding threshold that filters claims unsupported by the reference material. This directly satisfies the requirement that answers remain grounded in the technical manuals and prevents hallucinated content, unlike guardrails focused solely on harmful categories or denied topics.
- ✗
Bedrock Agents with a multi-step reasoning prompt
Why it's wrong here
Agents orchestrate multi-step reasoning and tool calls, but grounding answers in retrieved manuals requires the RetrieveAndGenerate API with citations, which constrains output to source documents. Agents suit tasks needing action sequencing, not citation-backed question answering over a static corpus.
Go deeper
Related to this question
About these practice questions
One of 862 original AIF-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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.