5.3 Azure AI Document Intelligence

Quick Answer: Document Intelligence (formerly Form Recognizer) extracts structured data from documents. Use Read for text, Layout for tables and structure, prebuilt models for common documents (invoices, receipts, IDs), and custom models for your own forms. Custom models are template (fixed layout) or neural (varying layout) and need at least five labeled samples. Combine a custom classifier with extraction models to auto-route mixed document types. Every field returns a confidence score.

Current Edition and Tooling

The service uses the v4.0 (2024-11-30 GA) REST API and SDKs, edited in Document Intelligence Studio (a no-code labeling portal). The Python client is DocumentIntelligenceClient, and analysis is asynchronous: begin_analyze_document returns a poller, and poller.result() blocks for the result. The old name Form Recognizer still appears in legacy docs and may show up as a distractor on the exam.

Choosing the Right Model

Decision shortcuts the exam rewards: only need raw text -> Read; need tables and checkboxes from any document -> Layout; the document is a standard invoice/receipt/ID -> the matching prebuilt; it is your company's unique form -> custom.

Read vs Layout vs General

poller = client.begin_analyze_document(model_id="prebuilt-layout", body=f)
result = poller.result()
for table in result.tables:
    print(table.row_count, "x", table.column_count)
for page in result.pages:
    for mark in page.selection_marks:
        print(mark.state)   # "selected" or "unselected"

Layout is the only no-training model that returns tables and selection marks (checkboxes), so any scenario mentioning checkbox state or table cells points to Layout, not Read.

Prebuilt Models with Confidence

Prebuilt models return strongly-typed fields, each with a confidence score between 0 and 1. Production code branches on a threshold and sends low-confidence results to human review.

poller = client.begin_analyze_document(model_id="prebuilt-invoice", body=f)
for doc in poller.result().documents:
    total = doc.fields.get("InvoiceTotal")
    if total and total.confidence >= 0.80:
        post_to_erp(total.content)
    else:
        route_to_human_review(doc)        # low confidence

Trap: the confidence value is a per-field reliability measure, not an accuracy guarantee. Exam scenarios that say "automatically post invoices over a reliability bar, otherwise queue for a person" are testing whether you branch on field.confidence against a threshold.

Custom Models: Template vs Neural

Both build modes need a minimum of five labeled training documents in a Blob container referenced by a Shared Access Signature (SAS) URL.

poller = client.begin_build_document_model(
    build_mode="neural",   # or "template"
    blob_container_url="https://acct.blob.core.windows.net/training?<SAS>",
    model_id="po-neural-v1")
model = poller.result()

If documents arrive from many vendors with shifting layouts, the answer is neural; if they are one rigid in-house form, template is cheaper and faster.

Classifiers and Routing Mixed Documents

When a single pipeline receives mixed document types, you build a custom classifier (begin_build_classifier) and then combine the classifier with extraction models so one begin_classify_document (or a classifier-driven analyze) classifies first, then routes each document to the correct extraction model. This v4.0 classifier approach is the modern successor to v3.0 composed models, which combined multiple custom extraction models behind one model ID and auto-selected among them.

poller = client.begin_classify_document(
    classifier_id="mailroom-classifier", body=document_bytes)
for doc in poller.result().documents:
    print(doc.doc_type, doc.confidence)   # e.g. "invoice" 0.97

Input Constraints and Security

Know the practical limits the exam likes to probe. Supported inputs include PDF, JPEG/JPG, PNG, BMP, TIFF, HEIF, and Office formats for some models. The free (F0) tier caps analysis at the first 2 pages of a document, while paid (S0) processes the whole file up to service limits. Training data must sit in Azure Blob Storage reached through a SAS URL (or a managed identity); the model itself never stores your source documents after training.

For security, prefer Microsoft Entra ID (managed identity) over the resource key so credentials are not embedded in code, and scope storage access with role-based access control. Use a customer-managed key when regulatory requirements demand control of encryption at rest.

On the Exam: A scenario describing a mailroom that receives invoices, receipts, and contracts and must process each correctly through one entry point is the classic classifier-plus-routing (composed-model) question — no manual pre-sorting step is needed. "Five documents" is the memorized minimum for custom training, and "varying layouts" always signals the neural build mode over template.