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.
6.1Implementing Text and Speech
Understand the two routes to implementing text and speech in Foundry—task-focused Foundry Tools (Azure AI Language / Azure AI Speech) and multimodal models that take audio directly—plus text analysis, speech-to-text (STT), text-to-speech (TTS), speech translation, and how to respond to spoken prompts.
Beyond generative apps and agents, Domain 2 expects you to implement text and speech solutions on Foundry. There are two main routes: (1) task-focused services—Azure AI Language (text analysis) and Azure AI Speech (speech) within Foundry Tools (formerly Azure AI services), which you connect to a project and call; and (2) multimodal models—generative models that accept audio/text directly, which you deploy and call. A useful split: "a fixed analysis (sentiment, transcription) → a dedicated service" vs "conversational spoken Q&A → a multimodal model."
6.1.1Implementing text analysis (Azure AI Language)
To process the meaning of text in a structured way, use the task-focused Azure AI Language. It is pretrained and provides the main text-analysis tasks with no model training: sentiment analysis (positive/negative), key phrase extraction, entity recognition (names, places, dates—NER), language detection, summarization, and PII detection. In Foundry you connect this service to a project and call it via endpoint + key (or Microsoft Entra ID auth) to build a lightweight text-analysis app. For fixed tasks it is cheaper, faster, and more predictable than a generative model.
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