Microsoft Azure AI Engineer Associate AI-102 Practice Question
You are building a custom question answering project in Azure Language Studio. After adding a PDF product manual as a knowledge source, many queries return the fallback answer "No answer found". Investigation shows the manual uses multiple heading levels and nested bullet lists rather than explicit FAQ pairs, so the default extraction misses most content. Without rewriting the manual, which configuration should you apply when adding the PDF so the service can create question-answer pairs from the document's hierarchy?
Upload a YAML file that defines custom extraction patterns together with the PDF.
Convert the PDF to a plain text (.txt) file and upload the text file instead.
Change the Classify file structure option to Structured before importing the PDF.
Add the built-in chit-chat personality to the knowledge base before testing.
When you add a file source, the Classify file structure setting (found under Advanced options) lets you tell the ingestion pipeline whether the document is structured, unstructured, or let it auto-detect. Setting this option to Structured forces the service to treat heading hierarchy and list items as distinct sections, enabling it to generate meaningful question-answer pairs from manuals and similar documents. Leaving Auto-detect may classify the file as unstructured, causing extraction to fail; converting to plain text would lose structure, chit-chat does not affect extraction, and YAML patterns are unsupported.
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Why does setting the file structure to 'Structured' improve extraction accuracy?
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What happens if Auto-detect is left as the file structure setting?
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Microsoft Azure AI Engineer Associate AI-102
Implement natural language processing solutions
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