Using Bedrock Guardrails with Kendra
A company is building a chatbot using Amazon Bedrock. They want to ensure the model's responses are grounded in company-specific data and that harmful content is filtered out. Which two services or features should they use? (Choose TWO.)
Quick Answer
This requirement splits into two genuinely separate problems, which is why two services are needed rather than one doing both jobs. Grounding responses in company-specific data is a retrieval problem: Amazon Kendra indexes the company's own document sources and, when paired with Bedrock, supplies the relevant passages as context the model draws its answer from, which is what keeps responses tied to real, authoritative company content instead of the model's general pre-training knowledge. Filtering harmful content is a completely different, safety-oriented problem: Bedrock Guardrails sits around the model's input and output, applying content filters and topic policies that block undesirable material regardless of whether that content came from company data or the model's own generation. Neither service substitutes for the other — Kendra grounds the content, Guardrails polices it — and a chatbot needing both grounded accuracy and content safety needs both configured together rather than assuming one implies the other. Any scenario naming both a data-grounding requirement and a safety/compliance requirement in the same breath is asking for this Kendra-plus-Guardrails pairing.
⚠ Common exam trap
AWS often tests the distinction between services that provide grounding (Kendra) versus those that orchestrate actions (Agents), and between content filtering (Guardrails) versus general NLP (Comprehend), leading candidates to confuse the roles of Bedrock Agents and Amazon Comprehend.
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 Kendra
Amazon Kendra is correct because it provides a managed search service that indexes company-specific data sources, enabling the Bedrock chatbot to retrieve relevant documents and ground its responses in authoritative information. Bedrock Guardrails is correct because it allows you to define content filters and topic policies to block harmful or undesirable outputs, ensuring the chatbot adheres to safety and compliance requirements.
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 Kendra
Why this is correct
Correct: Amazon Kendra can be used as a knowledge base for RAG to ground responses in company data.
- ✗
Bedrock Agents
Why it's wrong here
Wrong: Bedrock Agents help build autonomous agents but do not inherently provide grounding or content filtering.
- ✗
Amazon Comprehend
Why it's wrong here
Wrong: Amazon Comprehend is for NLP insights, not for grounding or filtering.
- ✓
Bedrock Guardrails
Why this is correct
Correct: Bedrock Guardrails allow content filtering to block harmful outputs.
- ✗
Amazon SageMaker JumpStart
Why it's wrong here
Wrong: SageMaker JumpStart provides pre-built models but not directly for grounding or filtering.
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Same concept, more angles
1 more way this is tested on AIF-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company uses Amazon Bedrock to power a generative chatbot for employee onboarding. Recently, some employees reported that the chatbot occasionally provides responses that contain biased or offensive language. The company has a strict policy for respectful communication. They want to implement a solution quickly without retraining the model. Which action should they take?
easy- A.Add a human reviewer to approve every response.
- B.Use a different foundational model known for unbiased outputs.
- ✓ C.Enable Amazon Bedrock's built-in content moderation filters.
- D.Fine-tune the model on a dataset of polite conversations.
Why C: Amazon Bedrock includes built-in content moderation filters that can be enabled to automatically detect and block biased or offensive language in model responses without requiring retraining or custom infrastructure. This allows the company to quickly enforce their respectful communication policy by filtering out harmful content at inference time, directly addressing the reported issue.
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.