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Last Updated: Sep 11, 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.
Apart from the training endorsed by the certification vendor, it’s a good idea to exhaust the study guides from Amazon. Thus, here’s what you need to know about the two must-have revision books for the Microsoft AI-900 certification exam prep:
This guide by David Voss is based on the current test curriculum and targets trainees looking to gain foundational knowledge of the AI solutions and services on Azure. It comprises practice test questions and explanations that are consistent with Microsoft’s online learning tools. So, if you want to prepare with confidence and pass your exam easily, get your Kindle copy today from Amazon for only around $12.
This book is a collection of skills and knowledge from a team of AI and Azure specialists with countless years of experience in the field. It is written by Microsoft Azure MVPs including Leila Etaati, Anupama Natarajan, Ashraf Ghonaim, Cameron Vetter, David Rendon, etc. who used their cumulative years of experience to design a definitive study guide and a solution for all trainees preparing for AI-900 exam. The authors state it categorically that this book does not intend to cover everything there’s to know about Microsoft AI. And that it is important to combine it with other study materials to have a better chance of passing the Microsoft AI-900 exam. In a nutshell, this guide targets AI and ML developers, architects, data scientists, .net developers, and consultants who are already familiar with the foundational AI concepts. Remember it is not your best guide if you are starting from a zero, but an excellent choice if you are already familiar with most of the concepts in this field.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
| Section | Weight | Objectives |
|---|---|---|
| Features of computer vision workloads on Azure | 15–20% | - Identify types of computer vision solutions - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Face - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Form Recognizer |
| Features of generative AI workloads on Azure | 20–25% | - Describe use cases for generative AI - Describe capabilities of Azure OpenAI Service - Describe responsible AI practices for generative AI - Describe generative AI concepts |
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Identify types of NLP solutions - Describe capabilities of Azure Speech - Describe capabilities of Azure Language - Describe capabilities of Azure Translator |
| Artificial Intelligence workloads and considerations | 15–20% | - Describe responsible AI principles - Describe considerations for developing AI solutions - Identify types of AI workloads |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe machine learning pipelines - Describe capabilities of Azure Machine Learning - Describe core concepts of machine learning - Describe automated machine learning |
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