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AIP-C01 practice question: Choosing and scaling vector stores (OpenSearch, Aurora pgvector, S3 Vectors, managed knowledge bases)

Foundation Model Integration, Data Management, and Compliance A free question from our original AIP-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.

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A four-person IT team at a mid-sized accounting firm is building a search assistant over about 80 GB of client-approved guides stored in Amazon S3 and a SharePoint library.

The team wants the assistant live within a few weeks and does not want to run, patch or tune a vector database. Staff often search for exact form numbers, so the search must combine keyword matching with meaning.

The documents change weekly, and the team wants the index kept up to date without writing sync code.

Which solution meets these requirements?

Choose one answer.

  1. A. Run a self-managed OpenSearch cluster with k-NN indexes, write a Lambda function that chunks, embeds and indexes each document, and expose a query API that the assistant calls.
  2. B. Create an Aurora PostgreSQL cluster with pgvector, run a scheduled job that embeds changed files into a table, and connect the assistant to it for similarity queries.
  3. C. Store the embeddings in Amazon S3 Vectors, which costs little at this scale, and write a retrieval Lambda function along with the connectors and sync logic for SharePoint and S3.
  4. D. Create an Amazon Bedrock Managed Knowledge Base with the built-in S3 and SharePoint connectors, let Bedrock manage the vector store, and rely on its hybrid keyword and semantic search.
Show the answer and explanation

Answer: D

  • A. Wrong: The cluster, the Lambda pipeline and the query API would all need to be built, tuned and patched by a small team. That misses the short timeline and the no-administration goal.
  • B. Wrong: Aurora with pgvector works well for vector search, but the team would own the database, the scheduled job and the change detection. It also needs extra work to combine exact form number matching with meaning. Learn more: Using Aurora PostgreSQL as a knowledge base for Amazon Bedrock (opens in a new tab)
  • C. Wrong: S3 Vectors is inexpensive for large vector collections, but it provides no SharePoint connector or sync logic, so the team would write both. It also leaves keyword matching for form numbers to the team, so the approach misses the setup goal. Learn more: Working with S3 Vectors (opens in a new tab)
  • D. Right: The managed knowledge base provides connectors and incremental sync, so the index updates without sync code, and Bedrock runs the vector store. Its built-in hybrid search matches form numbers as well as meaning, which meets every requirement. Learn more: Build a managed knowledge base (opens in a new tab)

Explanation

A managed knowledge base removes the vector database work and provides the S3 and SharePoint connectors with incremental sync. Its hybrid search handles exact form numbers alongside meaning. The self-managed, Aurora and S3 Vectors options each leave the team building sync, search or database operations.

Retrieval augmented generation with a knowledge base Sync: documents from S3 and other connectors are split into chunks, turned into embeddings, and stored in a vector store. Query: a user question retrieves the best matching chunks from the vector store, and the model writes an answer from them with citations. Sync Documents S3, connectors Chunk + embed embeddings Vector store managed or own Query User question from the app Retrieve best chunks Model answers with citations
The built-in connectors and a Bedrock-managed vector store mean the small team runs no search infrastructure.

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