AI-901T00 is Microsoft's updated course titled "Introduction to AI in Azure" — a 2025 replacement and evolution of the AI-900 fundamentals track. It introduces foundational AI concepts and gives hands-on experience with practical Azure AI services, including the brand-new Azure AI Foundry. The course is structured across 2 Learning Paths and 12 Modules: the first path builds conceptual AI knowledge, and the second path maps each concept directly onto Azure services so learners can start building immediately.

The course covers the full AI landscape — from core AI terminology and machine learning basics through generative AI, AI agents, computer vision, speech, natural language processing, and AI-powered document intelligence — all delivered via the Azure AI platform.

AI-901T00 Course Summary:

Course Title	Introduction to AI in Azure
Course Code	AI-901T00
Associated Certification	Microsoft Certified: Azure AI Fundamentals
Duration	1 Day (8 Hours)
Structure	2 Learning Paths • 12 Modules
Delivery	Instructor-Led Training (ILT) / Online
Exam Price	$99 (USD)
Passing Score	700 / 1000
Schedule Exam	Pearson VUE
Prerequisites	Basic computing knowledge; some Python familiarity; understanding of cloud concepts (storage, compute, authentication)
Official Course Page	learn.microsoft.com/en-us/training/courses/ai-901t00

Learning Path 1: AI Concepts & Fundamentals

Builds a strong conceptual foundation across six core AI domains — no Azure tools yet, purely understanding the ideas.

#	Module	Topics Covered
1	Introduction to AI Concepts	- What is Artificial Intelligence? - Core AI terminology: models, training, inference - Types of AI: Narrow AI vs General AI - Machine learning basics: supervised, unsupervised, reinforcement - Responsible AI principles: fairness, reliability, privacy, inclusiveness, transparency, accountability - Real-world AI use cases and industry applications
2	Introduction to Generative AI and Agents	- What is Generative AI? How large language models (LLMs) work - Text generation, code generation, and image generation concepts - Prompt engineering fundamentals - Retrieval Augmented Generation (RAG) concepts - What are AI Agents? Agentic AI patterns - How agents automate tasks and solve business problems - Multi-agent collaboration concepts
3	Introduction to Computer Vision	- How computer vision models interpret images - Image classification concepts - Object detection: bounding boxes and labels - Optical character recognition (OCR) concepts - Facial detection and analysis concepts - Semantic image segmentation overview - Video analysis and frame-level processing
4	Introduction to Speech	- How speech-to-text (transcription) works - How text-to-speech (synthesis) works - Real-time vs batch transcription - Speech translation concepts - Keyword recognition and voice activation - Custom voice and speaker recognition
5	Introduction to Natural Language Processing	- What is NLP and why it matters - Text tokenization and linguistic analysis - Sentiment analysis and opinion mining - Named entity recognition (NER) - Key phrase extraction - Language detection and translation concepts - Question answering and language understanding models - Conversational AI and chatbot concepts
6	Introduction to AI-Powered Information Extraction	- What is document intelligence (form/document processing)? - Extracting structured data from unstructured documents - Prebuilt models: invoices, receipts, ID documents, contracts - Custom document extraction models - Knowledge mining concepts: indexing and searching documents - AI-powered search and semantic ranking

Learning Path 2: Get Started with AI Applications on Azure

Maps every concept from Learning Path 1 directly onto Azure AI services — practical, hands-on, using Azure AI Foundry and Azure AI Studio.

#	Module	Topics Covered
7	Get Started with AI in Azure	- Overview of Azure AI Services portfolio - Introduction to Azure AI Foundry — the unified AI development platform - Azure AI Studio: creating projects, deploying models - Provisioning Azure AI resources and managing keys/endpoints - Connecting Azure AI services in applications (SDK & REST API) - Security: Azure Key Vault, managed identities, RBAC for AI resources - Monitoring and logging Azure AI services
8	Get Started with Generative AI and Agents in Azure	- Azure OpenAI Service: provisioning and deploying models (GPT-4o, DALL-E) - Using Azure AI Foundry to deploy and test LLMs - Prompt engineering in Azure OpenAI Playground - Implementing RAG with Azure AI Search + Azure OpenAI - Building AI Agents using Azure AI Agent Service - Azure AI Content Safety for responsible generative AI - Integrating OpenAI APIs in Python / C# applications
9	Get Started with Computer Vision in Azure	- Azure AI Vision service: image analysis, tagging, captioning - Object detection and OCR with Azure AI Vision - Azure AI Face service: face detection and verification - Custom image classification with Azure AI Custom Vision - Azure AI Video Indexer for video understanding - Calling Vision APIs from code (REST / SDK)
10	Get Started with Speech in Azure	- Azure AI Speech service: speech-to-text and text-to-speech - Real-time transcription and batch transcription - Speech translation using Azure AI Speech - Custom Speech models for domain-specific vocabulary - Implementing keyword recognition and intent detection - Using the Speech SDK in Python / C# projects
11	Get Started with Natural Language Processing in Azure	- Azure AI Language service: sentiment analysis, NER, key phrase extraction - Language detection and text translation with Azure AI Translator - Conversational language understanding (CLU) models - Question Answering projects with Azure AI Language - Building and deploying a bot with Azure Bot Service - Integrating language APIs in applications
12	Get Started with AI-Powered Information Extraction in Azure	- Azure AI Document Intelligence: prebuilt models (invoices, receipts, IDs) - Training and using custom document extraction models - Azure AI Search: creating indexes, data sources, skillsets - Built-in cognitive skills for AI-enriched search - Semantic search and vector search concepts in Azure AI Search - Knowledge store: persisting enriched data for downstream analytics

Key Learning Outcomes

• Understand core AI terminology, machine learning types, and responsible AI principles
• Explain how generative AI and large language models (LLMs) work and use Azure OpenAI Service
• Describe and implement AI agent patterns using Azure AI Agent Service
• Analyse images and videos using Azure AI Vision and Azure AI Video Indexer
• Process speech with Azure AI Speech for transcription, synthesis, and translation
• Analyse text using Azure AI Language (sentiment, NER, key phrases, Q&A)
• Extract data from documents using Azure AI Document Intelligence
• Build AI-powered search solutions with Azure AI Search
• Navigate and use Azure AI Foundry to develop and deploy AI solutions

To ensure success, we recommend completing the official Microsoft Learn modules, hands-on labs in Azure AI Foundry, and practice tests aligned to the Azure AI Fundamentals certification.