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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 dimensionality may improve retrieval but does not enforce grounding in generation.

  • Larger chunk sizes in the knowledge base

    Why it's wrong here

    Chunk size affects retrieval but does not force the model to stick to retrieved content.

  • Bedrock Guardrails with grounding support

    Why this is correct

    Grounding checks ensure the model's output is supported by the provided context (retrieved documents), reducing ungrounded or hallucinated answers.

  • Bedrock Agents with a multi-step reasoning prompt

    Why it's wrong here

    Agents reason but do not enforce grounding in retrieved documents.

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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.