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AIP-C01 practice question: Chunking strategies (fixed-size, hierarchical, semantic, custom Lambda)

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 payments company loads about 15,000 plain-text terms-and-conditions documents into a knowledge base. Each document is a uniform block of paragraphs with no headings, tables or nested sections.

Support agents ask questions whose answers can sit anywhere in a document, often across a sentence break. The team wants a chunking setting that is simple to configure and cheap to run, with no code to maintain.

The embedding cost should stay close to the number of tokens in the documents.

Which chunking strategy meets these requirements?

Choose one answer.

  1. A. Use hierarchical chunking with parent chunks of about 1,500 tokens and child chunks of about 300 tokens, so that each answer can be matched at the child level and expanded to its parent.
  2. B. Use semantic chunking, which uses embeddings to find topic boundaries in each document and creates chunks at those boundaries, for the best matches on long text.
  3. C. Write a custom transformation Lambda function that splits each document on paragraph breaks and attaches the chunk metadata, so that the chunks follow the paragraphs of each file.
  4. D. Use fixed-size chunking with chunks of about 300 tokens and a 20 percent overlap, so that each sentence near a boundary appears in two chunks and the document is split evenly.
Show the answer and explanation

Answer: D

  • A. Wrong: Hierarchical chunking works on any text, but it adds parent and child size settings and stores larger parent chunks that are returned in place of the matched child chunks. Uniform paragraphs gain nothing from that extra setup, so fixed-size chunking is the simpler fit. Learn more: Chunking strategies (opens in a new tab)
  • B. Wrong: Semantic chunking embeds the sentences to find boundaries, which adds embedding cost on top of the indexing cost for every document. It also misses the cost requirement that the embedding spend stay close to the token count.
  • C. Wrong: A custom Lambda splits well, but the team would write and maintain the code, which the requirement forbids. Paragraph splits also give uneven chunk sizes, which the uniform-text case does not need. Learn more: Use a custom transformation Lambda function (opens in a new tab)
  • D. Right: Fixed-size chunking is a built-in setting with no code to maintain, and it splits uniform text evenly. The overlap keeps sentences that cross a boundary whole, and the embedding cost stays close to the token count, which meets every requirement. Learn more: Chunking strategies (opens in a new tab)

Explanation

Uniform text needs no structure-aware splitting, so fixed-size chunks with overlap give even chunks, sentences that are not cut off, and a built-in setting with no code. Hierarchical, semantic and custom chunking each add structure, embedding cost or code that this content does not need.

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