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AIF-C01 practice question: RAG, knowledge bases and vector stores (4 of 4)

Applications of Foundation Models A free question from our original AIF-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.

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A university loads 2,000 long research reports into a knowledge base. Each report is split into smaller chunks, and each chunk is converted to an embedding before it is stored in a vector index.

What is the main purpose of this chunking step?

Choose one answer.

  1. A. So each report can be stored in a different AWS Region for resilience
  2. B. So the foundation model is fine-tuned separately on each chunk of text
  3. C. So only the passages relevant to a question are retrieved and fit in the prompt
  4. D. So each chunk is encrypted separately before it is embedded
Show the answer and explanation

Answer: C

  • A. Wrong: Regional placement is set by where the data and index are deployed. Chunking is about how text is retrieved, so it does not decide where reports are stored.
  • B. Wrong: A knowledge base retrieves chunks and adds them to the prompt. It does not fine-tune the model, which is a separate customization method.
  • C. Right: Correct. Each chunk gets its own embedding, so a search can return the few passages that answer a question, and they fit in the model's context. The mapping back to the source document is kept for citations. Learn more: How Amazon Bedrock knowledge bases work (opens in a new tab)
  • D. Wrong: Encryption protects stored and transmitted data and is configured separately from chunking. Chunking does not change how the data is protected.

Explanation

Chunking splits documents into manageable pieces before they are embedded and written to a vector index. Each chunk gets its own vector, so a search returns specific passages rather than whole long reports. Each chunk keeps a mapping back to its source document.

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