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AIP-C01 practice question: Connecting data sources and keeping vector stores current

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 software company's support assistant must answer from three sources: product pages on its public documentation website, runbooks in a Confluence space, and contracts in a SharePoint library.

The documentation team edits pages several times a day, and deleted pages must also disappear from the assistant's answers. The platform team has no spare engineers to build or run custom data pipelines.

The index should refresh each night, and each sync should copy only the changes.

Which solution meets these requirements with the LEAST custom development?

Choose one answer.

  1. A. Add the website, Confluence and SharePoint as data sources of one knowledge base, using the built-in web crawler and connectors, and schedule incremental syncs each night.
  2. B. Write a nightly script for each system that exports its pages to an S3 bucket, and point a single S3 data source at that bucket so that the knowledge base syncs from the exports.
  3. C. Create an AWS Glue crawler over each content system and point the knowledge base at the crawler output, so the Data Catalog keeps the documents current.
  4. D. Build a Step Functions workflow that calls each system's API every night, embeds every page with a Titan embeddings call, and writes the vectors to a self-managed index.
Show the answer and explanation

Answer: A

  • A. Right: The built-in connectors read each source directly, and incremental sync picks up added, changed and deleted content. No pipeline code is needed, which meets the freshness, deletion and effort requirements. Learn more: Crawl web pages for your knowledge base (opens in a new tab)
  • B. Wrong: The scripts would need to be written, monitored and fixed whenever an API changes, which the team cannot support. Deletions would also depend on the export script noticing removed pages.
  • C. Wrong: A Glue crawler builds Data Catalog table metadata from stored data, and it does not extract page text from Confluence or SharePoint. The content would not reach the knowledge base.
  • D. Wrong: The workflow is a full custom pipeline that the team would design, deploy and operate, and it would re-embed unchanged pages unless more logic is added. That misses the least-development requirement. Learn more: Connect to Amazon Bedrock with Step Functions (opens in a new tab)

Explanation

Built-in web crawler and SharePoint and Confluence connectors read each source directly, and incremental sync handles changes and deletions on a nightly schedule. Exporting scripts, a Glue crawler and a custom workflow each add development work or fail to load the content at all.

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
One knowledge base with three data sources, synced each night, answers from the website, Confluence and SharePoint together.

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