AI-102 Practice Question: Implement knowledge mining and information extraction solutions
Your organization is using Azure AI Document Intelligence to process expense reports. The reports are submitted as images and need to be classified into categories (e.g., travel, office supplies) before extraction. Which feature of Document Intelligence should you use?
⚠ Common exam trap
Test-takers frequently confuse the prebuilt expense report model (which extracts data) with the classification model (which categorizes documents), leading them to select Option D despite the question explicitly asking for classification before extraction.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Custom classification model
Azure AI Document Intelligence's custom classification model is specifically designed to categorize documents (such as expense report images) into user-defined classes (e.g., travel, office supplies) before any extraction occurs. This model uses a trained classifier to assign a document type based on its visual and textual features, enabling downstream processing with the appropriate extraction model.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Custom classification model
Why this is correct
A custom classification model trains on labelled samples to categorise documents into your own classes, such as travel or office supplies, before extraction. This satisfies the requirement to classify expense report images into categories prior to extracting fields.
- ✗
OCR capability
Why it's wrong here
OCR extracts text from images but does not classify documents.
- ✗
Layout extraction
Why it's wrong here
Layout extraction returns text, tables, selection marks and their coordinates, but assigns no category labels such as travel or office supplies. It is tempting because it underpins custom models, and would be correct when the requirement is structural OCR output rather than document classification.
- ✗
Prebuilt expense report model
Why it's wrong here
Prebuilt expense report model extracts fields such as totals and vendor names from already-identified expense documents; it performs no categorisation into travel or office supplies. It is tempting because it targets expense reports specifically, and would be correct once classification is complete and structured field extraction is the goal.
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