Last Updated: Aug 07, 2026
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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.
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This certification test has no official prerequisites. However, the interested candidates must develop their skills and knowledge in the domains of the exam topics. Although the individuals do not need this test to pursue more advanced Azure role-based options, they can gain extensive expertise while preparing for this exam. Your knowledge base can contribute to the success of more advanced certificates such as Microsoft Certified: Azure AI Engineer Associate or Microsoft Certified: Azure Data Scientist Associate.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
Here, the following skills will be measured:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of generative AI workloads on Azure | 20–25% | - Describe capabilities of Azure OpenAI Service - Describe generative AI concepts - Describe responsible AI practices for generative AI - Describe use cases for generative AI |
| Topic 2: Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe considerations for developing AI solutions - Describe responsible AI principles |
| Topic 3: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Language - Identify types of NLP solutions - Describe capabilities of Azure Translator - Describe capabilities of Azure Speech |
| Topic 4: Fundamental principles of machine learning on Azure | 15–20% | - Describe core concepts of machine learning - Describe automated machine learning - Describe machine learning pipelines - Describe capabilities of Azure Machine Learning |
| Topic 5: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Custom Vision - Identify types of computer vision solutions - Describe capabilities of Azure Face - Describe capabilities of Azure Form Recognizer |
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