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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.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
Microsoft AI-900 中文 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Fundamental principles of machine learning on Azure | 15–20% | - Describe machine learning pipelines - Describe capabilities of Azure Machine Learning - Describe automated machine learning - Describe core concepts of machine learning |
| Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe responsible AI principles - Describe considerations for developing AI solutions |
| Features of generative AI workloads on Azure | 20–25% | - Describe capabilities of Azure OpenAI Service - Describe use cases for generative AI - Describe responsible AI practices for generative AI - Describe generative AI concepts |
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Language - Describe capabilities of Azure Translator - Identify types of NLP solutions - Describe capabilities of Azure Speech |
| Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Face - Describe capabilities of Azure Computer Vision - Identify types of computer vision solutions - Describe capabilities of Azure Form Recognizer |
