Last Updated: Aug 16, 2026
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Microsoft AI-900 exam is an associate level certification which is a three to five-hour exam that covers the technologies necessary for building intelligent applications for cloud computing. Students should have a solid knowledge of Microsoft Azure IaaS, services, APIs, SQL Server 2017. Role of Azure Intelligent Solutions. Windows 2012, Windows 2016, and SQL Server 2017. .NET Framework 4.6.1 (or 4.7), or higher. Resident Deployment of the Solutions. Classify application workloads. Apply Azure monitoring and analytics, Operations Management Suite (OMS) logs, and Event Hubs. Manage the master database. Implement master database failover. Microsoft AI-900 exam dumps aligns with the objectives listed below. Classify application workloads. Deploy, configure, set up, activate, maintain, troubleshoot and support all necessary Azure services. Manage the master database.
Informationidentify the load of a given server. Forums and blogs about Azure and use of its products. Long term planning and development of future plans. Categoriesidentify the Azure deployment models. Forums and blogs about Azure and use of its products. Easy management of the Azure resources. Responsible for managing the database when the system doesn't work normally. Array of Solutionstests the knowledge of the candidate. Backgrounds of the candidate before they start their work. Push when you are in the process of testing.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe fundamental principles of machine learning on Azure | 30-35% | - Describe Azure Machine Learning capabilities - Describe core machine learning concepts - Describe features of no-code automated ML - Identify common machine learning tasks |
| Topic 2: Describe AI workloads and considerations | 15-20% | - Identify features of common AI workloads - Identify guiding principles for responsible AI |
| Topic 3: Describe features of computer vision workloads on Azure | 15-20% | - Identify Azure AI services for computer vision - Identify common computer vision tasks - Describe Azure capabilities for computer vision |
| Topic 4: Describe features of Generative AI workloads on Azure | 15-20% | - Identify responsible AI considerations for generative AI - Describe generative AI concepts - Describe Azure OpenAI Service capabilities |
| Topic 5: Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify Azure AI services for NLP - Describe Azure capabilities for NLP - Identify common NLP tasks |
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