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MLA-C02 practice question: Embeddings and document prep for RAG (2 of 2)

Data Preparation for ML and AI A free question from our original MLA-C02 practice exam. Try it, then open the answer to see why each option is right or wrong.

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A logistics broker is building a Bedrock knowledge base over PDF rate sheets. Each sheet holds tables of lane rates, fuel surcharges and accessorial fees, with a header row that repeats across pages.

When the knowledge base uses default fixed-size chunking on plain text extraction, answers quote rates from the wrong lane because a table row is split across two chunks. The fix must keep each table row intact and must not require the broker to convert the PDFs by hand.

Which change meets these requirements?

Choose one answer.

  1. A. Increase the chunk size so that most rate sheets fit into one chunk, and keep plain text extraction.
  2. B. Export every rate sheet to a spreadsheet by hand each month, and upload the spreadsheets to the data source in place of the PDFs.
  3. C. Choose a parsing option that understands tables in PDFs, such as Bedrock Data Automation or a foundation model parser, and chunk the parsed output.
  4. D. Switch the vector store from Amazon OpenSearch Serverless to Amazon Aurora PostgreSQL with pgvector, and keep the same parsing and chunking settings.
Show the answer and explanation

Answer: C

  • A. Wrong: Large chunks can hold a whole sheet, but retrieval then returns too much text for the model and dilutes the relevant lane. The stem asks to keep table rows intact in a retrievable form.
  • B. Wrong: This would handle tables, but manual conversion each month conflicts with the requirement. The team would also lose the source PDFs as the authoritative documents.
  • C. Right: Correct. The default parser extracts only plain text, while Bedrock Data Automation and foundation model parsers extract tables with their structure before chunking. The broker keeps the original PDFs and needs no manual conversion. Learn more: Parsing options for your data source (opens in a new tab)
  • D. Wrong: The vector store stores embeddings and does not decide how a PDF table is split into chunks. Changing it does not address the split row.

Explanation

The rows are split because the default parser flattens the PDF tables into plain text before chunking. A parsing option that understands tables, such as Bedrock Data Automation or a foundation model parser, preserves the table structure, and chunking then works on structured content. Larger chunks, manual spreadsheets and a different vector store do not change how the tables are parsed.

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