AI-102 Plan and manage an Azure AI solution Practice Question
You are planning to use Azure AI Document Intelligence to process a large volume of mixed document types (invoices, receipts, and purchase orders). The solution must automatically classify each document type and extract relevant fields. What should you configure?
⚠ Common exam trap
A common mix-up: candidates assume a single model (like neural or prebuilt) can both classify and extract, but Azure AI Document Intelligence requires a separate classification step before extraction for mixed document types.
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
✓
Create a custom classification model to identify document types, then use extraction models
Azure AI Document Intelligence (formerly Form Recognizer) requires a two-step process for mixed document types: first, a custom classification model identifies each document type (invoice, receipt, purchase order), then separate extraction models (custom or prebuilt) extract the relevant fields from each classified type. This approach ensures accurate routing and field extraction without relying on filenames or brittle regex patterns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the Form Recognizer service with neural models
Why it's wrong here
Form Recognizer is the old name; neural models do not classify.
- ✓
Create a custom classification model to identify document types, then use extraction models
Why this is correct
Custom classification model handles mixed types accurately.
- ✗
Use prebuilt models for each document type and route based on filename
Why it's wrong here
Filename routing is unreliable; prebuilt models don't handle classification.
- ✗
Use the Read model to extract all text and then use regular expressions to classify
Why it's wrong here
Complex and error-prone.
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