Microsoft Azure AI FundamentalsStudy guide

Understand what AI is, the relationship among AI/ML/deep learning, and the common AI workloads emphasized in AI-901—generative AI, agentic AI, text analysis, speech, computer vision, and information extraction—plus how Azure delivers them in a unified way on Microsoft Foundry.

Understand Microsoft’s six Responsible AI principles—fairness, reliability & safety, privacy & security, inclusiveness, transparency, accountability—and how to address risks specific to generative AI and agents (hallucination, harmful content, prompt injection).

Understand Microsoft Foundry, the unified platform for building and operating AI solutions—its portal, projects, model catalog, connected Azure AI services, playground, and evaluation/monitoring—plus how the portal and SDK divide the work.

Understand how to choose a model from the catalog (capability, modality, context length, cost), the flow of deploying to get an endpoint + key, the basics of tokens and cost, and trying/evaluating in the playground.

Understand how large language models (LLMs) generate text by predicting likely next tokens, and the basics of controlling output via prompt elements—system message, instructions, examples, and parameters.

Understand grounding—making a model answer based on up-to-date or organization-specific information it was not trained on—and its common implementation RAG (retrieval-augmented generation), plus how to wire it into an app with the Foundry SDK.

Understand that agentic AI extends generative AI with autonomous goal-directed planning, tool use, and multi-step execution; learn how it differs from simple generative AI, the building blocks (tools/function calling, knowledge, actions), and when agents fit.

Understand building agents with Foundry Agent Service in Microsoft Foundry (compose model + instructions + knowledge + tools), multi-agent orchestration, and safety practices for operating agents (permissions, approval, monitoring).

Understand the role of Azure Content Understanding—extracting structured fields/insights from diverse content (documents, forms, images, audio, video)—and the idea of a schema (fields) that defines what to extract.

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.

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.

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.