Exam AI-103 Topic 1 Question 38 Discussion
Actual exam question for Microsoft's AI-103 exam
Question #: 38
Topic #: 1
Question #: 38
Topic #: 1
You have a Microsoft Foundry project that contains an agent.
The agent uses a knowledge source built from documents stored in Azure Blob Storage. The documents include digitally scanned PDFs that contain multipage tables.
You have an ingestion job that extracts only plain text, causing loss of table structure, headings, and page-number metadata.
Users frequently ask questions that require the retrieval of specific table rows across the pages.
You need to configure an ingestion job for a Retrieval Augmented Generation (RAG) pipeline that performs optical character recognition (OCR) on scanned PDFs, preserves tables and headings as structure-aware chunks, and stores page-number metadata with each chunk.
How should you configure the ingestion job?
The agent uses a knowledge source built from documents stored in Azure Blob Storage. The documents include digitally scanned PDFs that contain multipage tables.
You have an ingestion job that extracts only plain text, causing loss of table structure, headings, and page-number metadata.
Users frequently ask questions that require the retrieval of specific table rows across the pages.
You need to configure an ingestion job for a Retrieval Augmented Generation (RAG) pipeline that performs optical character recognition (OCR) on scanned PDFs, preserves tables and headings as structure-aware chunks, and stores page-number metadata with each chunk.
How should you configure the ingestion job?
Suggested Answer: B Vote an answer
Use OCR and page-level chunking.
Structure Preservation: OCR combined with document layout analysis (such as layout-aware or page-level chunking) detects visual structures like tables, headers, and reading order.
Row-Level Granularity: Page-level chunking retains the boundaries and context of multipage tables, enabling the RAG system to isolate and retrieve specific rows effectively.
Metadata Enrichment: Processing documents page-by-page allows the ingestion pipeline to automatically tag each extracted text chunk with its source page-number metadata.
Incorrect:
[not C]
Use page-level OCR extraction and store each page as a single chunk:
While this captures page numbers, storing an entire page as a single chunk introduces too much noise. It prevents the model from pinpointing specific table rows and often exceeds the optimal context window size for precise RAG retrieval.
[Not D]
Use basic parsing and fixed-size chunking:
Basic parsing cannot read digitally scanned PDFs, resulting in empty or completely garbled text.
Furthermore, fixed-size chunking splits text at arbitrary character limits, which destroys table structures and splits individual rows across different chunks.
Reference:
https://pub.towardsai.net/unlocking-key-technologies-in-document-parsing-81bfe20d741b
Structure Preservation: OCR combined with document layout analysis (such as layout-aware or page-level chunking) detects visual structures like tables, headers, and reading order.
Row-Level Granularity: Page-level chunking retains the boundaries and context of multipage tables, enabling the RAG system to isolate and retrieve specific rows effectively.
Metadata Enrichment: Processing documents page-by-page allows the ingestion pipeline to automatically tag each extracted text chunk with its source page-number metadata.
Incorrect:
[not C]
Use page-level OCR extraction and store each page as a single chunk:
While this captures page numbers, storing an entire page as a single chunk introduces too much noise. It prevents the model from pinpointing specific table rows and often exceeds the optimal context window size for precise RAG retrieval.
[Not D]
Use basic parsing and fixed-size chunking:
Basic parsing cannot read digitally scanned PDFs, resulting in empty or completely garbled text.
Furthermore, fixed-size chunking splits text at arbitrary character limits, which destroys table structures and splits individual rows across different chunks.
Reference:
https://pub.towardsai.net/unlocking-key-technologies-in-document-parsing-81bfe20d741b
by Eleanore at Aug 27, 2026, 08:15 PM
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