Microsoft Azure AI FundamentalsStudy guide
The fundamentals certification for Azure AI and machine learning (AI-900).
About Microsoft Azure AI Fundamentals (AI-900)
Microsoft Azure AI Fundamentals (AI-900) is a Fundamentals-level certification from Microsoft. This page organizes the exam scope into a 5-chapter, 11-section study guide and lets you check your understanding with exam-style practice questions. A good flow is to read the chapters below in order, then test yourself via "Practice questions."
Exam domains (approximate weighting)
- AI workloads and considerations~19%
- Machine learning fundamentals~19%
- Computer vision~19%
- Natural language processing~19%
- Generative AI~24%
Weights are approximate guidance for the live exam. Each domain is covered in detail in the chapters and sections below.
Note: This exam is scheduled to retire on 2026-06-30 (successor: AI-901).
Official exam information: https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-900
1AI Overview and Responsible AI
- 1.1AI Workloads and Their Types
Understand what AI is, the relationship among AI/ML/deep learning, the common AI workloads on Azure—machine learning, computer vision, NLP, document intelligence, knowledge mining, and generative AI—and how Azure delivers them as prebuilt or custom services. The starting point for AI-900.
- 1.2Principles of Responsible AI
Understand Microsoft’s six Responsible AI principles—fairness, reliability & safety, privacy & security, inclusiveness, transparency, and accountability—and the Azure features/tools that support each, for using AI safely, fairly, and transparently.
2Machine Learning Fundamentals
- 2.1Types of Machine Learning
Understand the basics of machine learning and the difference between supervised learning (regression, classification) and unsupervised learning (clustering).
- 2.2Azure Machine Learning Services and Workflow
Understand the basic ML workflow (prepare data → train → evaluate → deploy → predict) and Azure Machine Learning’s no-code features (Automated ML and Designer).
3Computer Vision
- 3.1Computer Vision Tasks
Understand the common computer vision tasks—image classification, object detection, OCR, and face detection—and how they differ.
- 3.2Azure Computer Vision Services
Understand Azure’s computer vision services—Azure AI Vision, Face, and Custom Vision—and when to use each.
4Natural Language Processing
- 4.1Natural Language Processing Tasks
Understand the common NLP tasks—sentiment analysis, key phrase extraction, entity recognition, language detection, translation, and speech—and how they differ.
- 4.2Azure Language and Speech Services
Understand Azure’s NLP services—Azure AI Language, Azure AI Speech, and Azure AI Translator—and when to use each.
5Generative AI
- 5.1Generative AI and Large Language Models
Understand what generative AI is, how large language models (LLMs) work, and core concepts—prompts, completions, and tokens.
- 5.2Azure OpenAI Service and Copilot
Understand Azure OpenAI Service for using generative AI on Azure, and Microsoft Copilot, the generative AI assistant built into Microsoft products.
- 5.3Responsible Use and Risks of Generative AI
Understand generative-AI-specific risks (hallucination—plausible but wrong output, bias, copyright/privacy) and how to use it in line with Responsible AI principles.