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In case you're eager to get to know more about artificial intelligence facets & machine learning functions that relate to Microsoft Azure, then you're recommended to opt for AI-900 test.
Target Audience
The Microsoft AI-900 exam is designed for those individuals who have little to no experience in the world of IT. It is aimed at the students with both non-technical and technical backgrounds. They have basic programming experience and knowledge. However, they are not required to have software engineering or data science experience.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
How to Prepare For AI-900: Microsoft Azure AI Fundamentals
Preparation Guide for AI-900: Microsoft Azure AI Fundamentals
Introduction
Microsoft AI-900 exam is designed for Microsoft partners who are certified by Microsoft to integrate Microsoft Azure Intelligent Solutions into their own products and solutions. The objective of the certification is to verify that approved partners have the knowledge and skills to deploy, configure, set up, activate, maintain, troubleshoot, and support all necessary Azure services. Microsoft AI-900 exam dumps exam tests Questions are designed to identify the exam taker level of competence in the functions, features, and services of Azure. The Microsoft AI-900 Certification Exam 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, and NET Framework. Courses are available online or in-person depending on individual needs.
This article will cover the areas of knowledge required for this exam, how many questions are covered in each area, and who is taking this certification exam.
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Microsoft AI-900 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Describe features of computer vision workloads on Azure | 15-20% | - Describe Azure capabilities for computer vision - Identify common computer vision tasks - Identify Azure AI services for computer vision |
| 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 |
| Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Describe Azure capabilities for NLP - Identify Azure AI services for NLP - Identify common NLP tasks |
| Describe AI workloads and considerations | 15-20% | - Identify guiding principles for responsible AI - Identify features of common AI workloads |
| 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 |
