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Last Updated: Aug 13, 2026
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In general, AI-900 is a computer-based exam that’s available in English, Chinese, Japanese, Spanish, French, German, and Korean languages. It costs $99 for every attempt and can be scheduled with Certiport or Pearson VUE. Recently, the AI-900 exam curriculum has been updated to reflect the following skills as contained in the official study guide:
The Microsoft Certified: Azure AI Fundamentals qualification authenticates the learner's understanding and skills in AI & ML concepts along with associated Azure services. In particular, the individual with this certification is competent in demonstrating their understanding of various workloads related to those terms along with their implementation in Azure cloud computing services. In addition, this certification is an excellent way to get a step ahead in the preparation for other role-based Azure certificates as well as showcasing one's skill in the fundamentals of ML as well as AI. All in all, the Azure AI Fundamentals validation form Microsoft is for beginners and thus, doesn't require any technical knowledge and expertise like, for example, engineering for software. However, some understanding of programming will help you a lot to easily nail test AI-900.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
The Microsoft AI-900: Microsoft Azure AI Fundamentals exam is aimed at those professionals who want to demonstrate their knowledge of basic Machine Learning (ML) and Artificial Intelligence (AI) workloads. The test evaluates their understanding of how to implement both ML and AI on Azure. The candidates who pass this exam are eligible for the Microsoft Certified: Azure AI Fundamentals certification.
| Section | Weight | Objectives |
|---|---|---|
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Language - Describe capabilities of Azure Translator - Describe capabilities of Azure Speech - Identify types of NLP solutions |
| Artificial Intelligence workloads and considerations | 15–20% | - Describe responsible AI principles - Identify types of AI workloads - Describe considerations for developing AI solutions |
| Features of generative AI workloads on Azure | 20–25% | - Describe generative AI concepts - Describe responsible AI practices for generative AI - Describe capabilities of Azure OpenAI Service - Describe use cases for generative AI |
| Features of computer vision workloads on Azure | 15–20% | - Identify types of computer vision solutions - Describe capabilities of Azure Face - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Computer Vision |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe core concepts of machine learning - Describe capabilities of Azure Machine Learning - Describe machine learning pipelines - Describe automated machine learning |
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