🧭 Overview
Exam Code: AI-900
Certification: Microsoft Certified: Azure AI Fundamentals
Audience: Individuals with both technical and non-technical backgrounds who want to demonstrate foundational AI and ML knowledge on Azure
Prerequisites: None (basic understanding of cloud and AI concepts helpful)
Exam Format: Multiple choice, drag-and-drop, scenario-based questions
Passing Score: 700/1000
Time Limit: ~45–60 minutes
Official Page: Microsoft AI-900 Certification
✅ Skills Measured
| Domain | Weight |
|---|---|
| Describe AI workloads and considerations | 15–20% |
| Describe fundamental principles of machine learning on Azure | 30–35% |
| 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 | 15–20% |
🧱 Exam Topics Breakdown
1. AI Workloads and Considerations
Types of AI: machine learning, computer vision, NLP, Generative AI
AI vs traditional programming
Considerations: fairness, reliability, privacy, safety, inclusiveness, transparency
Microsoft’s responsible AI principles
2. Machine Learning on Azure
Supervised vs unsupervised learning
Features, labels, model training and evaluation
Azure Machine Learning: designer, workspaces, datasets, AutoML
Regression, classification, clustering basics
3. Computer Vision on Azure
Image classification, object detection, facial recognition
Azure services: Computer Vision, Custom Vision, Face API, Azure AI Vision Studio
Use cases: retail, healthcare, accessibility
4. Natural Language Processing (NLP) on Azure
Language understanding (LUIS), QnA Maker (merged into Azure AI Language)
Sentiment analysis, language detection, key phrase extraction
Azure AI Language and Azure AI Speech services
5. Generative AI on Azure
Azure OpenAI Service overview
Large language models (LLMs): GPT, Codex, DALL·E
Prompt engineering basics
Use cases: summarization, code generation, chatbots
🗺️ 4-Week Study Plan
| Week | Topics | Microsoft Learn Modules |
|---|---|---|
| Week 1 | Intro to AI and responsible AI | Explore AI fundamentals |
| Week 2 | Machine learning on Azure | Intro to ML with Azure |
| Week 3 | Computer Vision + NLP | Explore computer vision + Explore NLP |
| Week 4 | Generative AI + Review | Intro to Generative AI on Azure + Practice Test |
🎓 Microsoft Official Learning Resources
✅ Microsoft Learn Path – AI-900
✅ Practice Assessment for AI-900
✅ Instructor-led Course: AI-900T00
🧠 Study Tips
Use the free Azure trial to explore services hands-on: Azure ML Studio, Vision, and Language services
Watch the animations and diagrams in the Microsoft Learn modules for conceptual clarity
Focus on understanding what each AI service does and which use case fits it best
Learn how machine learning works conceptually — you won’t need to write code for the exam
Familiarize yourself with Microsoft’s Responsible AI principles
Practice recognizing different AI service names and scenarios
✅ Final Exam Checklist
Completed all Microsoft Learn modules
Passed Microsoft’s official practice assessment
Reviewed Responsible AI principles and key workloads
Practiced with Azure AI Studio or AI demo apps
Booked exam via Pearson VUE or Certiport
✍️ Concepts to Master
Supervised vs unsupervised learning
Model training, testing, evaluation basics
Azure AI services: Vision, Language, OpenAI, Azure ML
Fairness, transparency, and responsible AI design
Use cases of image classification, speech-to-text, text analytics
Understanding LLMs and prompt engineering
