AIF-C01 Applications of Foundation Models Practice Question
A company uses Amazon Bedrock to build a chatbot. The chatbot needs to answer questions based on internal company documents. Which AWS service should be integrated with Bedrock to enable Retrieval Augmented Generation (RAG) without managing infrastructure?
⚠ Common exam trap
AWS often tests the distinction between managed services that require infrastructure management (like OpenSearch Service) and fully managed services (like Kendra) that abstract away all infrastructure concerns, making candidates incorrectly choose OpenSearch for its search capabilities.
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 a fully managed intelligent search service that can be directly integrated with Amazon Bedrock to implement Retrieval Augmented Generation (RAG) without any infrastructure management. It indexes internal company documents and retrieves relevant passages, which are then passed to the foundation model as context to generate accurate, grounded answers.
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 OpenSearch Service
Why it's wrong here
OpenSearch Service requires provisioning and tuning clusters, so it does not meet the stem's serverless requirement. It is tempting because OpenSearch genuinely supports vector search for RAG, and would be correct if the company accepted managing infrastructure or used OpenSearch Serverless.
- ✗
Amazon DynamoDB
Why it's wrong here
DynamoDB stores items by primary key and cannot perform the semantic vector similarity search RAG retrieval needs. It is tempting because it is serverless and often holds the source documents, but it would be correct for storing conversation state or metadata, not embeddings retrieval.
- ✗
Amazon RDS
Why it's wrong here
Amazon RDS is a managed relational database, not a vector store, so it cannot supply the semantic retrieval that RAG requires without custom embedding and indexing infrastructure. It is tempting because RDS stores the source documents, and it would be the correct choice if the requirement were transactional storage rather than managed retrieval.
- ✓
Amazon Kendra
Why this is correct
Amazon Kendra provides a fully managed, ML-powered enterprise search service that indexes internal documents and returns relevant passages, which Bedrock then feeds to the foundation model for grounded answers. It satisfies the RAG requirement without infrastructure management, unlike self-managed vector stores or OpenSearch clusters.
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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.