AI-102 Plan and manage an Azure AI solution Practice Question
Your organization uses Azure AI Document Intelligence to extract data from invoices. The solution must identify custom fields not present in the prebuilt models, such as 'purchase order number' located in varying positions across documents. What should you do?
⚠ Common exam trap
It's easy for candidates to assume the prebuilt invoice model can be extended with custom fields via configuration or merging, but Azure AI Document Intelligence requires explicit custom model training to recognize fields not present in prebuilt schemas.
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
✓
Train a custom extraction model using labeled sample invoices.
Azure AI Document Intelligence (formerly Form Recognizer) allows you to train a custom extraction model using labeled sample invoices. This approach enables the model to learn custom fields like 'purchase order number' that appear in varying positions, which prebuilt models cannot handle. By providing labeled examples, the model generalizes to extract the field accurately from new documents.
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 layout model and apply manual post-processing.
Why it's wrong here
The layout model returns text, tables and selection marks without labelled fields, so 'purchase order number' cannot be extracted as a typed key-value pair; manual post-processing cannot reliably locate values in varying positions. It suits documents where only structure and reading order matter, not custom field extraction.
- ✗
Use Azure AI Forms Recognizer with prebuilt receipt model.
Why it's wrong here
The prebuilt receipt model extracts fixed receipt fields and cannot learn custom fields such as purchase order number. It tempts because receipts resemble invoices, but custom extraction requires training a custom model with labelled samples, which is the correct approach here.
- ✗
Use the prebuilt invoice model with field merging.
Why it's wrong here
The prebuilt invoice model recognises only its fixed schema of invoice fields; 'purchase order number' is absent, and field merging combines existing extracted fields rather than discovering new ones. It is the right choice when the required fields already fall within the prebuilt invoice schema.
- ✓
Train a custom extraction model using labeled sample invoices.
Why this is correct
Custom extraction models learn field labels and their positional context from your own labelled invoices, so they locate fields such as purchase order number wherever they appear. Prebuilt invoice models expose only a fixed schema, which cannot capture organisation-specific fields in varying positions.
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