A RAG system indexes financial reports that contain narrative text sections, data tables, and footnotes. Early testing shows that simple fixed-size chunking splits tables mid-row and separates footnotes from the text that references them, causing Claude to misinterpret financial figures. What chunking strategy preserves semantic integrity for this document type?

A is correct because financial documents have rich structure that fixed-size chunking destroys: tables are semantic units where row/column relationships are essential for interpreting figures; footnotes provide qualifications that change the meaning of the figures they annotate. Structure-aware chunking that respects these boundaries and attaches metadata (section headers, table captions) preserves the semantic integrity needed for accurate financial interpretation. B is wrong because reducing chunk size to 128 tokens makes table splitting worse, not better — smaller chunks are more likely to split mid-table, not less; and tables may exceed 128 tokens. C is wrong because overlapping chunks duplicate content (increasing index size and retrieval noise) and still split table rows mid-entry — overlap does not create semantic boundaries, only redundant coverage. D is wrong because converting tables to natural language loses the structured data format that makes table retrieval valuable — Claude can interpret table structure visually or through JSON; prose conversion loses precision and adds ambiguity.