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Last Updated: Sep 04, 2026
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High level of difficulty and many challenging questions. Identify the different types of AI models. AI design choosing tools. Who should take the exam? Artificial intelligence. Describe the strengths and weaknesses for artificial intelligence.
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Prove that you can describe the following: AI workloads and considerations; fundamental principles of machine learning on Azure; features of computer vision workloads on Azure; features of Natural Language Processing (NLP) workloads on Azure; and features of conversational AI workloads on Azure.
All in all, Microsoft provides two ways to help candidates prepare for the Microsoft AI-900 exam. These include the free online learning option and the paid, instructor-led training. Let’s discuss the two most popular courses offered by each of these options by the vendor:
The online gratis class, “Get Started with Artificial Intelligence” features one module and is intended to run for a maximum of 34 minutes. In general, this course is designed to help you get started with Microsoft services that empower new AI solutions. There are no prerequisites associated with this training. However, it features one module with a total of 10 units including the introduction and an understanding of machine learning, anomaly detection, computer vision, natural language processing, conversational AI, and responsible AI just to mention a few. Visit the official certification page to find out more information about this learning path that also includes classes on ML, NLP, and conversational AI.
This one-day course is part of the instructor-led learning platform and is, therefore, a paid option. As the name suggests, it is meant to introduce the candidates to fundamental AI concepts. The training, in particular, emphasizes the skills you need to become well versed in AI workloads and understand the Azure services that support such solutions. Usually, the course curriculum is viewed as comprehensive blended learning, featuring a combination of online materials and instructor-led training to ensure candidates have a good grasp of the exam topic areas. Even more, the hands-on exercises that are featured here are meant to help learners understand the test domains in real-world scenarios. Familiarity with the internet and computer technology is not a prerequisite but a great combination that will go a long way toward helping you register excellent results in this course and the ultimate exam.
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 |
|---|---|---|
| Describe features of computer vision workloads on Azure | 15-20% | - Identify common computer vision tasks - Describe Azure capabilities for computer vision - Identify Azure AI services for computer vision |
| Describe features of Generative AI workloads on Azure | 15-20% | - Describe generative AI concepts - Describe Azure OpenAI Service capabilities - Identify responsible AI considerations for generative AI |
| Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify Azure AI services for NLP - Identify common NLP tasks - Describe Azure capabilities for NLP |
| Describe fundamental principles of machine learning on Azure | 30-35% | - Describe features of no-code automated ML - Describe Azure Machine Learning capabilities - Describe core machine learning concepts - Identify common machine learning tasks |
| Describe AI workloads and considerations | 15-20% | - Identify guiding principles for responsible AI - Identify features of common AI workloads |
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