AI-900 Microsoft Azure AI Fundamentals Certification Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
This course provides a comprehensive, lab-focused preparation for the Microsoft AI-900 certification exam. You'll gain hands-on experience with core Azure AI services, explore real-world use cases, and build foundational AI skills across machine learning, computer vision, natural language processing, and conversational AI. The course integrates ethical AI principles throughout and concludes with practice exams and guided labs to reinforce learning. With approximately 6.5 hours of structured content, this course is designed for beginners seeking to validate their knowledge of Azure AI fundamentals.
Module 1: Introduction to Azure AI Workloads
Estimated time: 0.75 hours
- Types of AI workloads supported in Azure
- Business use cases for AI solutions
- Key Azure AI service categories
- Architectural considerations for AI workloads
Module 2: Machine Learning Fundamentals
Estimated time: 1 hour
- Core ML concepts: data pipelines and feature engineering
- Model training workflows in Azure
- Using Azure Machine Learning Studio for experiments
- Tracking and managing models in Azure
Module 3: Computer Vision Services
Estimated time: 1 hour
- Working with Custom Vision
- Using the Face API for facial recognition
- Applying Form Recognizer for document processing
- Hands-on lab: building and deploying a vision model
Module 4: Natural Language Processing
Estimated time: 1 hour
- Overview of Text Analytics service
- Language Understanding (LUIS) fundamentals
- Speech services in Azure
- Lab exercise: sentiment analysis and language translation pipelines
Module 5: Conversational AI
Estimated time: 1 hour
- Creating chatbots with QnA Maker
- Building bots using the Bot Framework
- Designing dialog flows
- Integrating bots with Azure Functions
Module 6: Practice Tests & Hands-On Labs
Estimated time: 1.25 hours
- Comprehensive practice exams covering AI-900 objectives
- Guided lab: deploying AI solutions
- Monitoring and maintaining AI models
- Ethical AI checkpoints and considerations
Prerequisites
- Basic understanding of cloud computing concepts
- Familiarity with Microsoft Azure fundamentals
- No prior AI or programming experience required
What You'll Be Able to Do After
- Identify appropriate Azure AI services for specific workloads
- Apply machine learning fundamentals using Azure tools
- Implement computer vision solutions with Azure services
- Build and deploy conversational AI chatbots
- Prepare confidently for the AI-900 certification exam