What's changed: Added per-section figures (cert-figure-retrofit). New AI-103 Chapter 6 (Azure Content Understanding = multimodal structured extraction/analyzer & schema/prebuilt vs custom/build flow, generative-prompt extraction of entities/topics/summaries = structured output/grounding & evaluation/structured vs schema extraction)
6.1Information Extraction with Azure Content Understanding
Understand implementing Azure Content Understanding to extract structured fields/insights from documents, forms, images, audio, and video—defining analyzers and schemas (extraction fields), prebuilt vs custom, and the build flow—from a developer’s view.
Azure Content Understanding extracts needed information in structured form from diverse content—documents, forms, images, audio, video (also covered in AI-901). At AI-103’s implementation level you create an analyzer (the whole extraction setup), define a schema (extraction field names/types), analyze files, and receive structured results (e.g., JSON). Use prebuilt for generic extraction and custom to define your own fields for org-specific forms.
| Step | What you do |
|---|---|
| 1. Create analyzer | For the target content type |
| 2. Define schema | Specify fields (name, type) |
| 3. Analyze | Pass files and run analysis |
| 4. Get results | Structured data with fields filled |
Common: (1) "structured extraction of fields from invoices/forms/images/audio/video" = Azure Content Understanding. (2) "define extraction fields" = the schema (part of the analyzer). (3) generic = prebuilt, org-specific forms = custom. (4) flow = analyzer → schema → analyze → structured results. (5) distinguish OCR (text recognition, Chapter 4) from structured extraction (typed fields).
Watch out: (1) OCR (text in images → text) vs information extraction (typed structured fields) differ in purpose. (2) analyzer (the setup) vs schema (field definitions)—the schema is part of the analyzer. (3) Quality depends on schema clarity and input quality. (4) Sensitive data needs privacy care (Chapter 1).
6.1.1Section summary
- Azure Content Understanding = files → structured data information extraction
- Build: create analyzer → define schema (fields) → analyze → structured results; prebuilt vs custom
- Distinguish OCR (text recognition) from structured extraction (typed fields); handle sensitive data with privacy care
Sign in to track progress — Log in.
Quick check
(just a quick review)Q1. Which Azure service extracts structured fields/insights from invoice PDFs, forms, images, audio, and video?
Q2. In Content Understanding, what defines "which fields, in what types, to extract"?
Q3. What is the basic build flow for an information-extraction app?
Q4. Which correctly contrasts OCR (Chapter 4) and Content Understanding extraction?
Keep track of your progress
The full study guide is free to read. Sign up free to practice with the question bank, track what you have read, review your mistakes, and highlight passages.