Implement an Azure AI Document Intelligence Solution
This section of the Microsoft AI-102: Designing and Implementing a Microsoft Azure AI Solution exam covers building and managing Azure AI Document Intelligence solutions. Below are study notes for each sub-topic, with links to Microsoft documentation, exam tips, and key facts
Provision a Document Intelligence Resource
📖 Docs: Create a Document Intelligence resource
Overview
- Document Intelligence (formerly Form Recognizer) extracts structured data from documents
- Provision in the Azure portal, CLI, or ARM template
- Resource includes endpoint + key for API access
Key Points
- Regional availability varies
- Pricing based on pages processed
- RBAC controls access
Exam Tip
Always provision Document Intelligence resource, not generic Cognitive Services
Use Prebuilt Models to Extract Data from Documents
📖 Docs: Document processing models
Overview
- Prebuilt models handle common document types:
- Invoices
- Receipts
- Business cards
- Identity documents
- Layout (general text extraction)
Key Points
- Quick start without training
- Outputs structured JSON with fields + confidence scores
- Useful for automating business workflows
Use Case
Extracting vendor, total, and date from an invoice
Implement a Custom Document Intelligence Model
📖 Docs: Document Intelligence custom models
Overview
- Custom models trained on user-provided documents
- Suitable when prebuilt models don’t fit requirements
Key Points
- Requires labeled training data
- Labeling done via Document Intelligence Studio
- Handles semi-structured or unstructured forms
Train, Test, and Publish a Custom Document Intelligence Model
📖 Docs: Build and train a custom extraction model
Overview
- Training creates a custom model from labeled docs
- Evaluation validates accuracy
- Publishing exposes endpoint for consumption
Key Points
- Supports iterative retraining for improvement
- Metrics include accuracy, recall, and precision
- Must publish before calling via API
Exam Tip
“Model trained but not available to apps” → must publish model
Create a Composed Document Intelligence Model
📖 Docs: Document Intelligence composed custom models
Overview
- Composed model = combines multiple custom models
- Automatically selects correct sub-model at runtime
Key Points
- Useful when multiple document types exist
- Reduces need to pre-sort documents
- Example: invoices + receipts + tax forms
Use Case
A composed model that routes between invoice and receipt extractors
Quick‑fire revision sheet
- 📌 Provision Document Intelligence resource, region + pricing matter
- 📌 Prebuilt models = invoices, receipts, IDs, business cards, layout
- 📌 Custom models require labeled data via Studio
- 📌 Must train, test, publish before API consumption
- 📌 Composed model = collection of multiple models auto-selected at runtime