AIF-C01 Applications of Foundation Models Practice Question
A developer is building a RAG-based Q&A bot with Amazon Bedrock Knowledge Bases. They need a managed vector store for document embeddings. Which service should they use?
⚠ Common exam trap
Candidates often confuse Amazon DynamoDB or Amazon RDS as viable options because they can store data, but they lack native vector search capabilities required for RAG, leading to an incorrect choice.
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 OpenSearch Serverless
Amazon Bedrock Knowledge Bases requires a vector store to store and query document embeddings for Retrieval-Augmented Generation (RAG). Amazon OpenSearch Serverless provides a managed, scalable vector engine that supports k-NN (k-nearest neighbor) search, making it the correct choice for this use case. It integrates natively with Bedrock Knowledge Bases to handle embedding storage and similarity search without manual infrastructure management.
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 Serverless
Why this is correct
Amazon OpenSearch Serverless provides a fully managed vector engine that Amazon Bedrock Knowledge Bases can use natively as its vector store, removing server provisioning and cluster scaling work. It satisfies the stem's managed vector store constraint for document embeddings, unlike self-managed alternatives requiring infrastructure upkeep.
- ✗
Amazon DynamoDB
Why it's wrong here
DynamoDB stores items by partition key, not high-dimensional vectors, so Bedrock Knowledge Bases cannot use it as a vector store. It is tempting because DynamoDB is a fully managed, scalable NoSQL database, and would be the right choice for storing session state, chat history, or metadata rather than embeddings.
- ✗
Amazon RDS
Why it's wrong here
Amazon RDS is a relational engine without native vector indexing or similarity search, so Bedrock Knowledge Bases cannot query embeddings from it. It is tempting as a managed database, and would be correct for structured transactional data such as customer records or order tables, not for approximate nearest-neighbour retrieval.
- ✗
Amazon S3
Why it's wrong here
Amazon S3 stores objects, not vectors, and Bedrock Knowledge Bases cannot query embeddings from it directly. S3 is the right choice for raw document storage feeding ingestion, but a managed vector store such as Amazon OpenSearch Serverless is needed for similarity search.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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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.