What's changed: Added per-section figures (cert-figure-retrofit). New AI-901 Chapter 5 (Domain 2: Content Understanding = multimodal information extraction (docs/forms/images/audio/video → structured), schema/field definition, prebuilt vs custom, positioning vs Document Intelligence/Vision/Speech, build flow = analyzer → schema → analyze → results, combining with RAG/agents)
5.2Building an Information-Extraction App
Understand the flow of building a lightweight information-extraction app with Content Understanding (create an analyzer → define the schema → analyze input → receive structured results), how it combines with Foundry and the SDK, and typical use cases.
The basics of building an extraction app: (1) create an analyzer → (2) define the extraction schema (fields) → (3) analyze files → (4) receive structured results. An analyzer bundles "which content type, with which schema, to extract." Pass a file to it and you get back results (e.g., JSON) with each field filled in. A common pattern: design and test the schema in the Foundry portal, then call analysis from your app via the SDK.
5.2.1The build flow
| Step | What you do |
|---|---|
| 1. Create analyzer | Create one for the target content type |
| 2. Define schema | Specify fields to extract (name, type) |
| 3. Analyze | Pass files and run analysis |
| 4. Get results | Receive structured data with fields filled |
5.2.2Combining with Foundry, generative AI, and agents
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