AIF-C01 Practice Question 25: Knowledge Base Vector Store

AWS Certified AI Practitioner (AIF-C01) · Test 3 · question 5 of 10

Applications of Foundation Modelsmedium

A developer is setting up an Amazon Bedrock knowledge base so a support assistant can retrieve passages from a few thousand PDF manuals. Nobody on the team has run a vector database before, and they would rather not create and tune an index by hand. Where should the document embeddings be stored?

Show answer and explanation

Answer:

B. In an Amazon OpenSearch Serverless vector index that Amazon Bedrock creates through quick create

A knowledge base needs a vector store: a database that indexes embeddings so it can return the chunks nearest to a query vector. Amazon Bedrock Knowledge Bases supports several (OpenSearch Serverless, OpenSearch managed clusters, Amazon Aurora, Amazon Neptune Analytics, Amazon S3 Vectors, Pinecone, Redis Enterprise Cloud, MongoDB Atlas), and it offers a quick-create flow in which Bedrock creates the vector index and configures the required vector, text and metadata fields for you. For a team with no vector database experience that is the intended path (option B).

- A is wrong because plain S3 objects have no vector index. The application would have to download and compare every embedding on every question, which does not scale and adds latency the knowledge base is meant to remove. - C is wrong because DynamoDB looks up items by key. It has no nearest-neighbour search, so it cannot answer "which chunks are semantically closest to this query". - D is wrong because Redshift is a data warehouse for analytical SQL over structured data. A join returns exact matches on a column, not the most similar vectors.

Reference: Prerequisites for using a vector store you created for a knowledge base

Practice Test 3 ›

Study guides for AIF-C01

AI engineer roadmap 2026: skills, certs and the orderAI engineer roadmap for 2026: skills to learn in order, AI engineer vs ML engineer, which certifications fit each stage, and what to skip.

How AI Is Changing Software Jobs: Where to PrepareHow AI is changing software jobs: what history shows, the evidence on junior developers, what Klarna learned, new AI roles and five domains to prepare for.

How AWS Certification Exams Work: Scoring and RenewalHow AWS certification exams work: levels, booking, question types, unscored questions, scaled scores, pass marks, validity and how recertification works.

What Is Amazon Bedrock? Models, Pricing and GuardrailsWhat is Amazon Bedrock? How a request reaches a model, what happens to your data, token pricing, Guardrails, Knowledge Bases, AgentCore and the exam traps.

Google Cloud Generative AI Leader vs AWS AI PractitionerGoogle Cloud Generative AI Leader vs AWS AI Practitioner: format, cost, topics, free preparation and which certification to take first.

AWS certification cost in 2026, and free ways to prepareWhat every AWS certification costs in 2026 in USD and INR, retake and reschedule rules, the 50% voucher, and the free official ways to prepare.

AWS Generative AI Developer Professional (AIP-C01) guideWhat the AWS Certified Generative AI Developer – Professional (AIP-C01) exam covers - format, cost, the five domains, the services, and how to prepare.

Best AI certifications 2026: AWS, Google, Microsoft, ClaudeThe best AI certifications in 2026 from AWS, Google, Microsoft, Claude and Databricks, by level, with price, status and which one to take first.

Which AWS certification should you get? 2026 roadmapWhich AWS certification to get first, by role and experience, with each exam's status in October 2026, what is changing, and prices in USD and INR.

What Is RAG? How Retrieval-Augmented Generation WorksWhat RAG is and how it works: chunking, embeddings, vector search, hybrid search, reranking, GraphRAG and agentic RAG on AWS, Google and Azure.

Prompt engineering vs RAG vs fine-tuningWhen to use prompt engineering, retrieval-augmented generation, fine-tuning or continued pre-training, compared on cost, freshness, hallucination and privacy.