What's changed: Added per-section figures (cert-figure-retrofit). New AI-901 Chapter 6 filling the missing Domain 2 sub-areas: implementing text and speech (Azure AI Language; Azure AI Speech = STT/TTS/translation; multimodal models for spoken prompts) and implementing computer vision and image generation (multimodal/Azure AI Vision = OCR/object detection; image-generation models). Appended as ch6 to keep existing ch1-5 relatedSections non-breaking.
6.2Implementing Computer Vision and Image Generation
Understand how to implement two capabilities in Foundry—"understanding images" (visual input) and "creating images" (image generation). Cover image interpretation/OCR via multimodal models or Azure AI Vision, and text-to-image generation via generative models, with selection guidance.
Image-related implementation has two distinct capabilities: (1) understand images (visual input)—recognize, describe, or classify the content of a photo or diagram; and (2) create images (image generation)—generate new images from a text instruction. For AI-901, don’t confuse the two and know how to implement each in Foundry. A memory aid: understanding is "image is the input," generation is "image is the output."
6.2.1Understanding images (visual input)
There are two routes to working with image content: (1) pass an image along with a prompt to a deployed multimodal model to flexibly interpret it—"what’s in this photo," "summarize this chart"; and (2) use the task-focused Azure AI Vision (Foundry Tools) for fixed analyses out of the box—OCR (text recognition, Read), object detection, image captioning, tagging, and face detection. Use a multimodal model for conversational/arbitrary questions, and Azure AI Vision for fixed extraction (especially text in images = OCR).
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