Microsoft Azure AI Fundamentals
Demonstrate foundational knowledge of machine learning and AI concepts and related Azure services.
Exam Details
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Step-by-Step RoadmapEstimated total: 2 to 3 weeks
AI & Machine Learning Fundamentals
Understand what AI is, the different types, and how machine learning works at a conceptual level.
What to Learn
- What is Artificial Intelligence?
- Machine Learning vs Deep Learning
- Supervised vs Unsupervised Learning
- Reinforcement Learning
- Regression vs Classification
- Neural Networks basics
Resources
- Microsoft Learn: AI Fundamentals module
- Azure AI Fundamentals YouTube playlist
You don't need to code for AI-900. Focus on concepts: what type of ML solves what problem. Classification = categories, Regression = numbers.
Computer Vision
Explore how machines see and understand images, and Azure's computer vision services.
What to Learn
- Image Classification
- Object Detection
- Facial Recognition
- Optical Character Recognition (OCR)
- Azure AI Vision
- Azure Face Service
- Video Analyzer
Resources
- Microsoft Learn: Computer Vision module
- Azure Vision Studio (hands-on demo)
Try Azure Vision Studio for free! Upload your own images and see how object detection and OCR work. This experiential learning sticks much better.
Natural Language Processing
Learn how AI understands and generates human language.
What to Learn
- Text Analysis
- Sentiment Analysis
- Key Phrase Extraction
- Language Detection
- Translation
- Azure AI Language
- Question Answering
- Language Understanding (CLU)
Resources
- Microsoft Learn: NLP module
- Azure Language Studio demo
The exam tests your knowledge of which Azure AI service to use for which NLP task. Make a quick reference table: service name → what it does.
Generative AI & Azure OpenAI
Understand large language models and Azure's integration with OpenAI services.
What to Learn
- What is Generative AI?
- Large Language Models (LLMs)
- Azure OpenAI Service
- ChatGPT & GPT-4
- DALL-E
- Prompt Engineering basics
- Copilots
Resources
- Microsoft Learn: Generative AI module
- Azure AI Foundry playground
This is a newer exam domain that's growing in importance. Know the difference between traditional ML (trained on labeled data) and generative AI (trained on huge unlabeled text).
Responsible AI
Understand Microsoft's principles for responsible AI development and deployment.
What to Learn
- 6 Microsoft AI Principles: Fairness, Reliability, Privacy, Inclusiveness, Transparency, Accountability
- AI Bias
- Explainability
- AI Ethics
Resources
- Microsoft Responsible AI principles page
- Microsoft Learn: Responsible AI module
Memorize the 6 Responsible AI principles! They almost always appear on the exam. Mnemonic: FRIPPTA = Fairness, Reliability, Inclusiveness, Privacy, Transparency, Accountability.
Practice & Exam
Complete practice tests and take the real exam.
What to Learn
- Practice exams
- Azure AI Service identification
- Scenario-based questions review
Resources
- Microsoft Official Practice Assessment for AI-900
- Whizlabs AI-900 tests
Many questions are scenario-based: 'A company wants to do X, which Azure AI service should they use?' Know the services and their use cases cold.