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AIF-C01 Practice Question: A data scientist is designing a RAG pipeline…
A data scientist is designing a RAG pipeline using Amazon Bedrock Knowledge Bases. They need to store embeddings of document chunks and perform similarity searches. Which vector store is a serverless option that integrates directly with Bedrock Knowledge Bases?
⚠ Common exam trap
Candidates often confuse 'serverless' with 'managed' and select a third-party option like Pinecone or MongoDB Atlas, which are managed but not natively integrated with Bedrock Knowledge Bases, or choose Aurora with pgvector thinking its serverless variant qualifies, but Bedrock Knowledge Bases does not support it as a direct vector store integration.
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 natively integrates with Amazon OpenSearch Serverless as a vector store for storing embeddings and performing similarity searches. OpenSearch Serverless is a fully serverless option that automatically scales and requires no infrastructure management, making it the correct choice for a serverless vector store that works directly with Bedrock Knowledge Bases.
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, serverless vector engine that Bedrock Knowledge Bases supports as a native vector store, so embeddings are indexed and queried without provisioning clusters. This satisfies the stem's serverless constraint, unlike self-managed OpenSearch or provisioned alternatives requiring capacity planning.
- ✗
Amazon Aurora with pgvector
Why it's wrong here
Aurora with pgvector requires you to provision and manage database instances, so it is not serverless; the question demands a serverless vector store. It is tempting because pgvector genuinely supports Bedrock Knowledge Bases as a vector store, and Aurora Serverless v2 exists, but that is still a provisioned cluster rather than a fully serverless offering.
- ✗
MongoDB Atlas
Why it's wrong here
MongoDB Atlas is a third-party database that Bedrock Knowledge Bases does not natively integrate with as a vector store; only OpenSearch Serverless, Aurora, Neptune Analytics and Pinecone are supported. It is tempting because Atlas offers Atlas Vector Search and a serverless tier, but native Bedrock integration is absent.
- ✗
Pinecone
Why it's wrong here
Pinecone is serverless and does integrate with Bedrock Knowledge Bases, but it is a third-party service rather than a native AWS serverless option, which the scenario implies. It is tempting because Pinecone is purpose-built for vector similarity search and is a legitimate Bedrock vector store choice in many architectures.
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.