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AI-900 validates foundational knowledge of machine learning and artificial intelligence concepts and the related Azure AI services. It covers AI workloads and responsible AI, fundamental machine learning principles, computer vision, natural language processing, and generative AI on Azure. Passing AI-900 earns the Microsoft Certified: Azure AI Fundamentals certification and is suitable for both technical and non-technical candidates.
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About this exam
Exam AI-900 measures foundational knowledge of AI and machine learning concepts and Azure AI services: AI workloads and responsible AI principles, machine learning fundamentals, computer vision, natural language processing, and generative AI. The skills measured reflect the outline updated May 2, 2025.
Who should take this exam
Anyone with technical or non-technical backgrounds who wants to demonstrate foundational knowledge of AI and machine learning concepts and Azure AI services. Data science and software engineering experience are not required, though awareness of basic cloud concepts helps.
Career benefits
Earning Azure AI Fundamentals demonstrates a solid grasp of AI concepts and Azure AI services, providing a foundation for role-based certifications such as Azure AI Engineer Associate and Azure Data Scientist Associate.
How to prepare
Study AI workload types and responsible AI principles (fairness, reliability and safety, privacy and security, inclusiveness, transparency, accountability), machine learning techniques (regression, classification, clustering, deep learning, Transformers), Azure Machine Learning, Azure AI Vision and Face, Azure AI Language and Speech, and generative AI services including Azure OpenAI and Azure AI Foundry. Use Microsoft Learn paths and the practice assessment.
Quick facts
Exam cost$99 USD
Valid forDoes not expire
Length45 minutes
Questions on exam40
Passing score700 / 1000
FormatMultiple choice, multiple response, drag-and-drop matching/ordering, and scenario items.
Practice questions150
Objectives5
Official pageView
What's covered
1. Describe Artificial Intelligence workloads and considerations
15–20%- 1a Identify features of common AI workloads
- 1b Identify guiding principles for responsible AI
2. Describe fundamental principles of machine learning on Azure
15–20%- 2a Identify common machine learning techniques
- 2b Describe core machine learning concepts
- 2c Describe Azure Machine Learning capabilities
3. Describe features of computer vision workloads on Azure
15–20%- 3a Identify common types of computer vision solution
- 3b Identify Azure tools and services for computer vision tasks
4. Describe features of Natural Language Processing (NLP) workloads on Azure
15–20%- 4a Identify features of common NLP workload scenarios
- 4b Identify Azure tools and services for NLP workloads
5. Describe features of generative AI workloads on Azure
20–25%- 5a Identify features of generative AI solutions
- 5b Identify generative AI services and capabilities in Microsoft Azure
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