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What does Amazon Bedrock say its default chunking approach does?

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  1. What does Amazon Bedrock say its default chunking approach does?
    • Splits text into roughly 300-token chunks while preserving complete sentence boundaries
    • Splits every document into one token
    • Uses one chunk for every knowledge base regardless of size
    • Groups documents by alphabetical title
  2. Why can the buffer size matter in semantic chunking?
    • It changes the source document’s spelling
    • It determines how many neighboring sentences are included when creating embeddings
    • It identifies the correct speaker
    • It specifies the model’s output vocabulary
  3. 매우 작은 청크와 매우 큰 청크 사이의 주요 절충점은 무엇입니까?
    • 작은 청크는 항상 삽입 속도가 느립니다.
    • 큰 청크는 절대 검색할 수 없습니다.
    • 작은 덩어리는 정확하지만 맥락을 잃습니다. 큰 덩어리는 맥락을 유지하지만 관련성을 희석시킵니다.
    • 의미있는 차이는 없습니다
  4. In Amazon Bedrock, what may its parsed-content chunker preserve as logical boundaries?
    • Only individual characters
    • Image colors and crop boxes
    • The prompt’s output format
    • Pages or sections
  5. How does Amazon Bedrock describe hierarchical chunking at retrieval time?
    • It retrieves child chunks and can replace them with broader parent chunks
    • It discards parent text and never returns broader context
    • It merges all documents into one vector
    • It converts every chunk to an image
  6. How does Amazon Bedrock’s semantic chunking propose boundaries between sentences?
    • By sorting sentences alphabetically
    • Using sentence-distance thresholds to identify less-similar adjacent sentences
    • By splitting every document at the same character count
    • By detecting speaker identity
  7. What did a 2026 chunking study find about overlap in its tested Natural Questions setup?
    • It always doubled answer accuracy
    • It eliminated all retrieval errors
    • It increased indexing cost without a measurable answer-quality benefit
    • It reduced indexing cost to zero
  8. What happened when retrieved context grew beyond about 2,500 tokens in that study?
    • Accuracy rose without limit
    • All questions became exact matches
    • The retriever stopped using embeddings
    • Answer quality showed a context cliff