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AI-901: Microsoft Azure AI Fundamentals

Course Summary

The AI-900: Microsoft Azure AI Fundamentals certification coourse covers foundational AI concepts, responsible AI principles, and hands-on implementation of AI solutions using Microsoft Foundry. Topics span generative AI, natural language processing, speech, computer vision, information extraction, and agentic AI — all within the Azure ecosystem.

Prerequisites:

  • Conceptual understanding of AI solutions on Azure
  • Familiarity with Python coding syntax and basic programming techniques
  • General familiarity with Azure resources and the Azure portal
Purpose
Learn core AI concepts  and responsible AI principles while preparing for the AI-901 certification exam
Audience
IT  Professionals at the beginning of their career in AI solution development
Role
Software Developers | Software Engineers | Business Analysts
Skill level
Beginner
Style
Lecture | Hands-on Activities | Labs
Duration
1 day 
Related technologies
Cloud | Python | Micrisift Foundry

 

Leasrning objectives
  • Describe the principles of responsible AI
  • Explain how generative AI models work
  • Identify common AI workload scenarios including generative AI, agentic AI, text analysis, speech, computer vision, and information extraction
  • Create effective prompts and deploy models using the Microsoft Foundry portal and SDK
  • Implement single-agent solutions and client applications using the Foundry portal and SDK
  • Extract information from documents, images, audio, and video using Azure Content Understanding in Foundry Tools

What you'll learn:

In this AI-901: Microsoft Azure AI Fundamentals course, you'll learn:

Identify AI Concepts and Capabilities 

  • Describe principles of 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
  • Identify AI model components and configurations
    • Describe how generative AI models work
    • Identify an appropriate AI model, based on capabilities
    • Identify appropriate model deployment options and configuration parameters
  • Identify AI workloads
    • Identify scenarios for common AI workloads, including generative and agentic AI, text analysis, speech, computer vision, and information extraction
    • Describe common text analysis techniques, including keyword extraction, entity detection, sentiment analysis, and summarization
    • Identify features and capabilities of speech recognition and speech synthesis
    • Identify features and capabilities of computer vision and image-generation models
    • Identify techniques to extract information from text, images, audio, and videos

Implement AI Solutions by using Microsoft Foundry 

  • Implement generative AI apps and agents by using Foundry
    • Create effective system and user prompts for generative AI models
    • Deploy a model and interact with it in the Foundry portal
    • Create a lightweight chat client application by using the Foundry SDK
    • Create and test a single-agent solution in the Foundry portal
    • Create a lightweight client application for an agent
  • Implement AI solutions for text and speech by using Foundry
    • Build a lightweight application that includes text analysis
    • Respond to spoken prompts by using a deployed multimodal model
    • Build a lightweight application by using Azure Speech in Foundry Tools
  • Implement AI solutions with computer vision and image-generation capabilities by using Foundry
    • Interpret visual input in prompts by using a deployed multimodal model
    • Create new visual outputs by using generative models
    • Build a lightweight application that includes vision capabilities
  • Implement AI solutions for information extraction by using Foundry
    • Extract information from documents and forms by using Azure Content Understanding in Foundry Tools
    • Extract information from images by using Content Understanding
    • Extract information from audio and video by using Content Understanding
    • Build a lightweight application with information extraction capabilities by using Content Understanding 

AI-901: Microsoft Azure AI Fundamentals

real world content

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
expert practitioners

Expert Practitioners

Industry experts that bring their battle scars into the classroom.
experiential learning

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
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