What's changed: Added per-section figures (cert-figure-retrofit). Rewrote AI-103 Chapter 4 to align with official skills measured (Apr 16, 2026). Shifted from AI-102-style (prebuilt Vision + Custom Vision) to a generation-centric design: s1 = image/video generation and editing (text→image: GPT-image-1/DALL·E, inpainting = mask editing, reference media, generation/editing controls, text→video: Sora, async); s2 = multimodal understanding (captions/visual QA/alt-text/object & region ID) + Content Understanding visual characteristic extraction (single-task/pro-mode, video analysis) + multimodal Responsible AI (Content Safety image moderation, indirect prompt injection via in-image text, visual policy: watermarks/prohibited symbols/brand)
4.2Multimodal Understanding and Responsible AI
Understand using multimodal models to comprehend visual context (captions, visual question answering, accessibility alt-text), extracting visual characteristics with Azure Content Understanding, and Responsible AI specific to multimodal content (filters for unsafe visual content, indirect prompt injection via text embedded in images, and visual policy such as watermarks).
A second pillar alongside generation is understanding images and videos. In AI-103, before building a dedicated recognition model, the default is to pass an image to a multimodal model (a generative model that accepts images) and understand it flexibly via a prompt. You can offload recognition and reasoning together—"describe this image," "where is the anomaly in this diagram?"
4.2.1Visual understanding with multimodal models
- Captions: generate image descriptions—concise or detailed, for a single image or multiple images.
- Visual question answering (visual QA): answer questions grounded in the image (based on what the image shows).
- Alt-text / extended descriptions: generate alt-text and detailed descriptions aligned to accessibility guidelines.
- Identify objects, components, and regions: locate targets (parts, regions) within images or video.
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