AI-900: Microsoft Azure AI Fundamentals
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Exam Resources
Official learning paths, exam details, skills measured, and community resources to supplement your study.
Exam Quick Facts
| Detail | Value |
|---|---|
| Exam Code | AI-900 |
| Title | Microsoft Azure AI Fundamentals |
| Level | Fundamentals |
| Pass Score | 700 / 1000 |
| Duration | 45 minutes |
| Questions | ~40–60 (multiple choice, drag-and-drop) |
| Cost | $99 USD (varies by region) |
| Scheduling | Pearson VUE |
| Skills Updated | May 2, 2025 |
| ⚠️ Retires | June 30, 2026 → replaced by AI-901 |
Official Learning Paths
- 📘 Get started with artificial intelligence — AI workloads, responsible AI principles
- 📘 Explore visual tools for machine learning — Regression, classification, clustering
- 📘 Explore computer vision — Image classification, object detection, OCR, face detection
- 📘 Explore natural language processing — Key phrase extraction, sentiment analysis, translation
- 📘 Explore generative AI — Azure OpenAI, AI Foundry, prompt engineering
📖 Study Resources
| Resource | Link |
|---|---|
| 📝 Official Exam Page | Microsoft Learn — AI-900 |
| 📖 Official Study Guide | Microsoft Study Guide |
| 🎯 Free Practice Assessment | Start Practice Assessment |
| 🖥️ Exam Sandbox | Try the exam interface |
| 🎬 Exam Readiness Zone | Video prep series |
Skills at a Glance
| Skill Area | Weight |
|---|---|
| Describe Artificial Intelligence workloads and considerations | 15–20% |
| Describe fundamental principles of machine learning on Azure | 15–20% |
| Describe features of computer vision workloads on Azure | 15–20% |
| Describe features of Natural Language Processing (NLP) workloads on Azure | 15–20% |
| Describe features of generative AI workloads on Azure | 20–25% |
Who is this exam for?
The AI-900 is Microsoft’s entry-level AI certification. It’s designed for anyone who wants to understand the fundamentals of artificial intelligence and how it’s implemented on Azure — whether you’re technical or non-technical. You don’t need data science or programming experience, though basic cloud knowledge helps.
This exam covers five key AI areas: general AI concepts, machine learning, computer vision, natural language processing, and generative AI. The generative AI section (Azure OpenAI, AI Foundry) is the newest and carries the most weight.
⚠️ Note: This exam retires June 30, 2026. The replacement (AI-901) will have a stronger focus on generative AI and Azure AI Foundry.
Skills Measured — with Microsoft Learn Links
Describe Artificial Intelligence workloads and considerations (15–20%)
This domain covers what AI is, the different types of AI workloads (vision, language, document processing, generative), and the principles of responsible AI. Microsoft takes responsible AI seriously — expect questions about fairness, transparency, accountability, and safety.
Identify features of common AI workloads
- Identify computer vision workloads
- Identify natural language processing workloads
- Identify document processing workloads
- Identify features of generative AI workloads
Identify guiding principles for responsible AI
Microsoft defines six principles for responsible AI. You need to know all six and understand how they apply in practice. Expect scenario-based questions like “Which principle is violated if an AI model produces biased results?”
- Describe considerations for fairness in an AI solution
- Describe considerations for reliability and safety in an AI solution
- Describe considerations for privacy and security in an AI solution
- Describe considerations for inclusiveness in an AI solution
- Describe considerations for transparency in an AI solution
- Describe considerations for accountability in an AI solution
Describe fundamental principles of machine learning on Azure (15–20%)
Machine learning is the foundation of most AI. This section covers the three main ML techniques (regression, classification, clustering), deep learning and the Transformer architecture, and Azure Machine Learning capabilities. You don’t need to build models — just understand the concepts and when to use each technique.
Identify common machine learning techniques
- Identify regression machine learning scenarios
- Identify classification machine learning scenarios
- Identify clustering machine learning scenarios
- Identify features of deep learning techniques
- Identify features of the Transformer architecture
Describe core machine learning concepts
- Identify features and labels in a dataset for machine learning
- Describe how training and validation datasets are used in machine learning
Describe Azure Machine Learning capabilities
- Describe capabilities of automated machine learning
- Describe data and compute services for data science and machine learning
- Describe model management and deployment capabilities in Azure Machine Learning
Describe features of computer vision workloads on Azure (15–20%)
Computer vision teaches machines to “see” and interpret images. This section covers four main scenarios — image classification (what’s in the image?), object detection (where are things in the image?), OCR (reading text from images), and facial detection. Azure AI Vision and Azure AI Face are the key services.
Identify common types of computer vision solution
- Identify features of image classification solutions
- Identify features of object detection solutions
- Identify features of optical character recognition solutions
- Identify features of facial detection and facial analysis solutions
Identify Azure tools and services for computer vision tasks
- Describe capabilities of the Azure AI Vision service
- Describe capabilities of the Azure AI Face detection service
Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)
NLP is how machines understand and generate human language. This section covers text analytics (key phrase extraction, entity recognition, sentiment analysis), language modelling, speech services (speech-to-text and text-to-speech), and translation. Azure AI Language and Azure AI Speech are the key services.
Identify features of common NLP Workload Scenarios
- Identify features and uses for key phrase extraction
- Identify features and uses for entity recognition
- Identify features and uses for sentiment analysis
- Identify features and uses for language modeling
- Identify features and uses for speech recognition and synthesis
- Identify features and uses for translation
Identify Azure tools and services for NLP workloads
- Describe capabilities of the Azure AI Language service
- Describe capabilities of the Azure AI Speech service
Describe features of generative AI workloads on Azure (20–25%)
This is the newest and highest-weighted domain. It covers generative AI models (like GPT), common scenarios (content generation, code assistance, image creation), responsible AI considerations specific to generative AI, and Azure services including Azure AI Foundry and Azure OpenAI Service. This section was significantly updated in May 2025 to reflect the rapid evolution of generative AI.
Identify features of generative AI solutions
- Identify features of generative AI models
- Identify common scenarios for generative AI
- Identify responsible AI considerations for generative AI
Identify generative AI services and capabilities in Microsoft Azure
- Describe features and capabilities of Azure AI Foundry
- Describe features and capabilities of Azure OpenAI service
- Describe features and capabilities of Azure AI Foundry model catalog
Quick Links
Skills at a Glance
| Skill Area | Weight |
|---|---|
| Describe Artificial Intelligence workloads and considerations | 15-20% |
| Describe fundamental principles of machine learning on Azure | 15-20% |
| Describe features of computer vision workloads on Azure | 15-20% |
| Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% |
| Describe features of generative AI workloads on Azure | 20-25% |
Skills Measured
Describe Artificial Intelligence workloads and considerations (15–20%)
Identify features of common AI workloads
- Identify computer vision workloads
- Identify natural language processing workloads
- Identify document processing workloads
- Identify features of generative AI workloads
Identify guiding principles for responsible AI
- Describe considerations for fairness in an AI solution
- Describe considerations for reliability and safety in an AI solution
- Describe considerations for privacy and security in an AI solution
- Describe considerations for inclusiveness in an AI solution
- Describe considerations for transparency in an AI solution
- Describe considerations for accountability in an AI solution
Describe fundamental principles of machine learning on Azure (15-20%)
Identify common machine learning techniques
- Identify regression machine learning scenarios
- Identify classification machine learning scenarios
- Identify clustering machine learning scenarios
- Identify features of deep learning techniques
- Identify features of the Transformer architecture
Describe core machine learning concepts
- Identify features and labels in a dataset for machine learning
- Describe how training and validation datasets are used in machine learning
Describe Azure Machine Learning capabilities
- Describe capabilities of automated machine learning
- Describe data and compute services for data science and machine learning
- Describe model management and deployment capabilities in Azure Machine Learning
Describe features of computer vision workloads on Azure (15–20%)
Identify common types of computer vision solution
- Identify features of image classification solutions
- Identify features of object detection solutions
- Identify features of optical character recognition solutions
- Identify features of facial detection and facial analysis solutions
Identify Azure tools and services for computer vision tasks
- Describe capabilities of the Azure AI Vision service
- Describe capabilities of the Azure AI Face detection service
Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)
Identify features of common NLP Workload Scenarios
- Identify features and uses for key phrase extraction
- Identify features and uses for entity recognition
- Identify features and uses for sentiment analysis
- Identify features and uses for language modeling
- Identify features and uses for speech recognition and synthesis
- Identify features and uses for translation
Identify Azure tools and services for NLP workloads
- Describe capabilities of the Azure AI Language service
- Describe capabilities of the Azure AI Speech service
Describe features of generative AI workloads on Azure (20–25%)
Identify features of generative AI solutions
- Identify features of generative AI models
- Identify common scenarios for generative AI
- Identify responsible AI considerations for generative AI
Identify generative AI services and capabilities in Microsoft Azure
- Describe features and capabilities of Azure AI Foundry
- Describe features and capabilities of Azure OpenAI service
- Describe features and capabilities of Azure AI Foundry model catalog
Quick Links
Frequently asked questions
AI-900 retires 30 June 2026 — same day as PL-600. The questions I’m hearing right now are mostly ‘should I rush AI-900 or just wait for AI-901?’
Should I still take AI-900 before it retires? #
What's the difference between AI-900 and AI-901? #
Will my AI-900 cert still be valid after the exam retires? #
How long does it take to prepare for AI-900? #
Is AI-900 enough to start a career in AI? #
Watch & Learn
Video courses to help you prepare.
Compare AI-900 across AWS & Google Cloud → Cert Compass
Frequently Asked Questions
1. Should I still take AI-900 before it retires?
Only if you can sit it before **2026-06-30** AND you can prep in the next 2 to 4 weeks. Once retired, AI-900 stays on your transcript permanently (Fundamentals certs don't expire) but stops being listed in new search results. If you're brand new to Azure AI today, go straight to [AI-901](/cert-tracker/ai-901/) — Microsoft's updated replacement covers Foundry, Copilot, and current generative AI services that AI-900 (last updated May 2025) doesn't fully cover.
2. What's the difference between AI-900 and AI-901?
[AI-901](/cert-tracker/ai-901/) is the updated AI Fundamentals exam — same level, same 45-minute / 40-60 question format, $99 cost. AI-901 has a stronger focus on generative AI (Azure OpenAI, Foundry, Copilot), agents, and current prompt engineering patterns. AI-900's content is still mostly accurate but missing the post-2025 services. If you're starting prep now, AI-901 is the safer pick — it'll stay current longer.
3. Will my AI-900 cert still be valid after the exam retires?
Yes — Microsoft Fundamentals certs don't expire. Your transcript continues to show 'Microsoft Certified: Azure AI Fundamentals' with the date you earned it. The badge stays on LinkedIn and on your Microsoft transcript. The retirement just means new candidates can't sit it from 1 July 2026 onward. Existing holders aren't downgraded — but recruiters will increasingly look for AI-901 on newer CVs.
4. How long does it take to prepare for AI-900?
Two to four weeks of part-time study for most beginners — assuming you've done one of Microsoft's free [learning paths](https://learn.microsoft.com/en-us/training/courses/ai-900t00). The exam covers 5 AI areas: general AI workloads, machine learning, computer vision, NLP, and generative AI. The generative AI section (Azure OpenAI, Foundry) is the largest and most recently added — also the section most likely to feel outdated by the time you sit the exam.
5. Is AI-900 enough to start a career in AI?
Honest answer — no. AI-900 (or AI-901) is the vocabulary cert. It signals you understand Azure AI services exist and roughly what they do. To land an AI-adjacent role you need either an associate cert ([AI-102](/cert-tracker/ai-102/) for engineers, [AI-200](/cert-tracker/ai-200/) for cloud developers, [AB-731](/cert-tracker/ab-731/) for business strategy) plus a portfolio of small built projects (Copilot Studio agents, Azure OpenAI POCs, prompt engineering case studies). The fundamentals cert alone gets your CV past the keyword filter.