AI-900 Microsoft Azure AI Fundamentals Exam Prep in 5 Hours

Are you ready to earn your Microsoft Certified: Azure AI Fundamentals credential?

This course is your complete, up-to-date guide to passing the AI-900 exam — covering every domain Microsoft tests, including the newest Generative AI and Azure OpenAI topics added in 2025.

The AI-900 exam is an opportunity to demonstrate knowledge of machine learning and AI concepts and related Microsoft Azure services — and it's designed for both technical and non-technical backgrounds, with no data science or software engineering experience required. Microsoft Learn

Whether you're a business professional, IT consultant, student, or career-switcher, this course will take you from zero to certified.

What You'll Learn:

• AI Workloads & Considerations — understand common AI use cases and responsible AI principles

• Machine Learning on Azure — core ML concepts and Azure Machine Learning fundamentals

• Computer Vision on Azure — image classification, object detection, and Azure AI Vision services

• Natural Language Processing (NLP) — text analytics, translation, and Azure Language services

• Generative AI — Azure OpenAI Service, GPT models, Copilot, and responsible AI practices Microsoft Learn

Why This Course?

The 2025 version of the AI-900 has a new emphasis on Generative AI, with exam questions covering Azure OpenAI Service, GPT, Copilot, and responsible AI — topics not always reflected in older practice materials. Medium This course is fully updated to cover all of it.

The AI-900 is one of the more accessible fundamentals exams, but you'll still want to be solid on machine learning types, responsible AI principles, generative AI use cases, computer vision, and Azure's language services. Pluralsight

Who This Course Is For:

• IT professionals and cloud beginners wanting to break into AI

• Business stakeholders and consultants seeking AI literacy

• Students pursuing a Microsoft certification pathway

• Anyone who wants a foundation before tackling the Azure AI Engineer Associate or Azure Data Scientist Associate

By the end of this course, you will:

• Understand core AI and ML concepts on Azure

• Know when and how to use each Azure AI service

• Be fully prepared to sit and pass the AI-900 exam

• Earn a globally recognised Microsoft certification

Once you pass, the certification doesn't expire — and it provides a solid foundation for a career in AI or data on Microsoft Azure. P

Skills at a glance

• 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%)

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

This exam is an opportunity for you to demonstrate knowledge of machine learning and AI concepts and related Microsoft Azure services. As a candidate for this exam, you should have familiarity with Exam AI-900’s self-paced or instructor-led learning material.

This exam is intended for you if you have both technical and non-technical backgrounds. Data science and software engineering experience are not required. However, you would benefit from having awareness of:

• Basic cloud concepts

• Client-server applications