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 LearnWhether 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 principlesMachine Learning on Azure — core ML concepts and Azure Machine Learning fundamentalsComputer Vision on Azure — image classification, object detection, and Azure AI Vision servicesNatural Language Processing (NLP) — text analytics, translation, and Azure Language servicesGenerative AI — Azure OpenAI Service, GPT models, Copilot, and responsible AI practices Microsoft LearnWhy 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. PluralsightWho This Course Is For:IT professionals and cloud beginners wanting to break into AIBusiness stakeholders and consultants seeking AI literacyStudents pursuing a Microsoft certification pathwayAnyone who wants a foundation before tackling the Azure AI Engineer Associate or Azure Data Scientist AssociateBy the end of this course, you will: Understand core AI and ML concepts on AzureKnow when and how to use each Azure AI serviceBe fully prepared to sit and pass the AI-900 examEarn a globally recognised Microsoft certificationOnce you pass, the certification doesn't expire — and it provides a solid foundation for a career in AI or data on Microsoft Azure.

PSkills at a glanceDescribe 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 workloadsIdentify computer vision workloadsIdentify natural language processing workloadsIdentify document processing workloadsIdentify features of generative AI workloadsIdentify guiding principles for responsible AIDescribe considerations for fairness in an AI solutionDescribe considerations for reliability and safety in an AI solutionDescribe considerations for privacy and security in an AI solutionDescribe considerations for inclusiveness in an AI solutionDescribe considerations for transparency in an AI solutionDescribe considerations for accountability in an AI solutionDescribe fundamental principles of machine learning on Azure (15-20%)Identify common machine learning techniquesIdentify regression machine learning scenariosIdentify classification machine learning scenariosIdentify clustering machine learning scenariosIdentify features of deep learning techniquesIdentify features of the Transformer architectureDescribe core machine learning conceptsIdentify features and labels in a dataset for machine learningDescribe how training and validation datasets are used in machine learningDescribe Azure Machine Learning capabilitiesDescribe capabilities of automated machine learningDescribe data and compute services for data science and machine learningDescribe model management and deployment capabilities in Azure Machine LearningDescribe features of computer vision workloads on Azure (15–20%)Identify common types of computer vision solutionIdentify features of image classification solutionsIdentify features of object detection solutionsIdentify features of optical character recognition solutionsIdentify features of facial detection and facial analysis solutionsIdentify Azure tools and services for computer vision tasksDescribe capabilities of the Azure AI Vision serviceDescribe capabilities of the Azure AI Face detection serviceDescribe features of Natural Language Processing (NLP) workloads on Azure (15–20%)Identify features of common NLP Workload ScenariosIdentify features and uses for key phrase extractionIdentify features and uses for entity recognitionIdentify features and uses for sentiment analysisIdentify features and uses for language modelingIdentify features and uses for speech recognition and synthesisIdentify features and uses for translationIdentify Azure tools and services for NLP workloadsDescribe capabilities of the Azure AI Language serviceDescribe capabilities of the Azure AI Speech serviceDescribe features of generative AI workloads on Azure (20–25%)Identify features of generative AI solutionsIdentify features of generative AI modelsIdentify common scenarios for generative AIIdentify responsible AI considerations for generative AIIdentify generative AI services and capabilities in Microsoft AzureDescribe features and capabilities of Azure AI FoundryDescribe features and capabilities of Azure OpenAI serviceDescribe features and capabilities of Azure AI Foundry model catalogThis 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.