Requisites of Microsoft AI-900 Exam
In order to take the Microsoft AI-900 exam, you must have a current Azure subscription. You also need a valid Microsoft ID. In addition, be sure to pass the following exams:
- AZ-100: Fundamentals of Azure
- AZ-200: Azure Fundamentals Assessment
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Microsoft Azure AI Fundamentals Exam Certification Details:
| Books / Training | Course AI-900T00: Microsoft Azure AI Fundamentals |
| Exam Name | Microsoft Certified - Azure AI Fundamentals |
| Sample Questions | Microsoft Azure AI Fundamentals Sample Questions |
| Passing Score | 700 / 1000 |
| Exam Code | AI-900 |
| Duration | 60 mins |
| Schedule Exam | Pearson VUE |
| Number of Questions | 40-60 |
| Exam Price | $99 (USD) |
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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Microsoft AI-900 中文 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Fundamental principles of machine learning on Azure | 15–20% | - Describe machine learning pipelines - Describe automated machine learning - Describe capabilities of Azure Machine Learning - Describe core concepts of machine learning |
| Topic 2: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Face - Describe capabilities of Azure Computer Vision - Identify types of computer vision solutions |
| Topic 3: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Language - Describe capabilities of Azure Translator - Describe capabilities of Azure Speech - Identify types of NLP solutions |
| Topic 4: Features of generative AI workloads on Azure | 20–25% | - Describe responsible AI practices for generative AI - Describe generative AI concepts - Describe capabilities of Azure OpenAI Service - Describe use cases for generative AI |
| Topic 5: Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe considerations for developing AI solutions - Describe responsible AI principles |



