easyMultiple Choice
AIF-C01 Practice Question: An application needs to store and search vector…
An application needs to store and search vector embeddings of 10 million documents for a RAG system. Which Amazon vector store is a fully managed, serverless option that integrates natively with Amazon Bedrock Knowledge Bases?
⚠ Common exam trap
A common pitfall is choosing Amazon Aurora PostgreSQL with pgvector because it is a managed database service, but it requires manual scaling and lacks native integration with Amazon Bedrock Knowledge Bases. Amazon OpenSearch Serverless is the fully managed, serverless option that automatically scales and natively integrates for RAG workloads.
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 integrates natively with Amazon Bedrock Knowledge Bases. It supports vector search for embeddings and automatically scales compute and storage capacity, making it ideal for RAG workloads with 10 million documents without requiring 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 Aurora PostgreSQL with pgvector
Why it's wrong here
Aurora PostgreSQL with pgvector is a provisioned relational database; the team manages instance classes, storage, and scaling rather than consuming a serverless vector store. It is tempting because pgvector does support embeddings and Bedrock integration, and would suit teams wanting SQL alongside vectors.
- ✗
MongoDB Atlas
Why it's wrong here
MongoDB Atlas is a third-party database, not an AWS-native service, so it lacks the native Amazon Bedrock Knowledge Bases integration the scenario requires. It is tempting because Atlas Vector Search genuinely provides managed embedding storage and similarity search, and would fit a multi-cloud or existing-MongoDB deployment.
- ✓
Amazon OpenSearch Serverless
Why this is correct
Amazon OpenSearch Serverless provides a fully managed, serverless vector engine that scales without capacity planning and integrates natively with Amazon Bedrock Knowledge Bases as a supported vector store, satisfying both the serverless constraint and the ten-million-document scale.
- ✗
Pinecone
Why it's wrong here
Pinecone is a third-party, fully managed vector database, but it is not an Amazon service and Bedrock Knowledge Bases does not natively integrate with it as a vector store. It is tempting because it is serverless and purpose-built for vectors, yet the question requires an AWS-native option.
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.