easyMultiple Choice
AIF-C01 Practice Question: A developer wants to store and search vector…
A developer wants to store and search vector embeddings for a RAG application. Which AWS-managed vector store option is serverless and can be used with Amazon Bedrock?
⚠ Common exam trap
AIF-C01 often tests whether candidates know which AWS services are actually serverless vector stores integrated with Bedrock, luring them toward DynamoDB or RDS because those are familiar 'managed' services.
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 OpenSearch Serverless is a fully managed, serverless vector store that supports k-NN vector search and is a natively supported backend for Amazon Bedrock Knowledge Bases. It requires no cluster provisioning or capacity planning, making it the correct serverless option for storing and searching embeddings in a RAG application.
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 RDS for MySQL
Why it's wrong here
RDS for MySQL stores relational rows, not vectors, and offers no native similarity search or vector index, so Bedrock cannot retrieve embeddings from it. It is tempting because RDS is a managed AWS database, but it suits transactional SQL workloads, not RAG vector storage.
- ✗
Amazon Redshift
Why it's wrong here
Redshift is a provisioned data warehouse for SQL analytics, lacking native vector similarity search and serverless embedding storage for Bedrock. It is tempting as a managed AWS analytics service, but it suits columnar reporting queries, not RAG vector retrieval.
- ✗
Amazon DynamoDB
Why it's wrong here
DynamoDB is a key-value store without native vector indexing or similarity search, so it cannot serve Bedrock embeddings for RAG retrieval. It is tempting as a serverless AWS database, but it suits low-latency item lookups, not nearest-neighbour vector queries.
- ✓
Amazon OpenSearch Serverless
Why this is correct
Amazon OpenSearch Serverless provides a fully managed, serverless vector engine with no cluster capacity to provision, and it integrates natively with Amazon Bedrock as a knowledge base vector store. This satisfies the serverless and Bedrock-compatible constraints for storing and searching embeddings.
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 and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.