> ## Documentation Index > Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt > Use this file to discover all available pages before exploring further. # Course Introduction > Overview of an Azure AI Fundamentals course covering core AI concepts, generative AI, agents, information extraction, responsible AI, hands-on labs, and AI-901 exam preparation Welcome to the Azure AI Fundamentals course. I'm Rithin Skaria, and I'm excited to help you develop the practical skills needed to understand AI and start building with it on Azure. AI is everywhere — in the apps we use, the tools we work with, and the products companies ship every day. This course prepares you for the [AI-901: Microsoft Azure AI Fundamentals](https://learn.kodekloud.com/user/courses/ai-901-microsoft-azure-ai-fundamentals) exam, which replaces the older [AI-900: Microsoft Certified Azure AI Fundamentals](https://learn.kodekloud.com/user/courses/ai-900-microsoft-azure-ai-fundamental). The [AI-901: Microsoft Azure AI Fundamentals](https://learn.kodekloud.com/user/courses/ai-901-microsoft-azure-ai-fundamentals) exam focuses on how teams build, deploy, and manage AI on Azure using a unified environment for modern enterprise AI workflows. A graphic card with a Microsoft Certified "Azure AI Fundamentals" badge on the left. On the right it says "THE NEW EXAM" against a light blue background. What changed with AI-901 * AI-901 emphasizes the end-to-end ecosystem: a unified environment for model cataloging, deployment, testing, and operations. * The course focuses on practical, industry-aligned workflows so you can apply skills immediately in development or operations roles. Course overview — what you’ll learn * Core AI concepts and common workloads: generative AI, agents, natural language processing (NLP), speech, and computer vision. A presentation slide titled "What Are AI Agents?" showing two panels: "Models" (understand language, reason, generate responses) and "Instructions" (role and objectives, behavior, constraints). A small circular presenter video thumbnail appears in the bottom right. * Information extraction from documents, images, and audio: techniques and Azure tools to convert raw content into structured, actionable data. A presentation slide titled "Natural Language Processing (NLP) – Introduction" highlighting "Named entity recognition" with an example sentence about Emma ordering a wireless keyboard on March 5th. A presenter’s video thumbnail appears in the lower-right corner. * Responsible AI practices: fairness, privacy, security, transparency, and accountability — and how to embed them into real projects. * Hands-on with the unified Azure environment (model catalog, deployment, prompt testing, workspace navigation), and practical labs to deploy models and build applications. * Generative AI apps and agents: build chat applications, action-taking agents, and learn prompt engineering to get the results you need. A split-screen slide titled "Azure AI Fundamentals" with bullet points like AI Concepts & Workloads, Responsible AI, and Generative AI on the left, and a presenter wearing a "KodeKloud" T-shirt gesturing with his hands on the right. * Multimodal solutions and information extraction: combine vision and speech, use document intelligence and OCR to extract meaning from real-world files. Course modules at a glance | Module | Focus | What you’ll practice | | - | - | - | | AI Concepts & Workloads | Overview of generative AI, agents, NLP, vision, speech | Identify the best AI approach for a task | | Responsible AI | Fairness, privacy, transparency, security | Apply governance and mitigation strategies | | Model Catalog & Deployment | Unified Azure workspace, model lifecycle | Deploy, test, and monitor models | | Generative AI & Agents | Chat apps, action-oriented agents, prompts | Build and evaluate prompts and agents | | Information Extraction | Document intelligence, OCR, vision & speech | Extract structured data from files and media | | Exam Prep & Labs | AI-901 exam topics and practical labs | Hands-on practice in Microsoft Foundry and Azure | Learning outcomes * Explain core AI concepts and common workloads on Azure. * Use Microsoft Foundry and Azure tools to find, test, and deploy models. * Build simple generative AI apps and agents, and craft prompts for desired outputs. * Extract structured information from documents, images, and audio with Azure services. * Apply Responsible AI principles to design safer, more trustworthy solutions. * Prepare confidently for the AI-901 certification. Hands-on practice is key. As you follow the labs, focus on end-to-end workflows: choose a model, deploy it, test prompts, and evaluate results with responsible AI checks. Get involved At CodeCloud, we value community. Join our forums to meet fellow learners, ask questions, and share your progress. Collaborating with peers helps you solidify concepts and accelerate your learning. Ready to master Azure AI fundamentals and advance your AI career? Dive into the next lesson to start exploring models and building your first experiment in the unified Azure AI environment. This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.