- Exam Code: AI-900
- Exam Name: Microsoft Azure AI Fundamentals (AI-900中文版)
- Certification Provider: Microsoft
- Corresponding Certification:Microsoft Certified: Azure AI Fundamentals
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The Azure AI Fundamentals certification might not be a prerequisite for any other certification levels, however, it can still be used to prepare for multiple Azure-related qualifications including the Microsoft Certified: Azure Data Scientist Associate and the Microsoft Certified: Azure AI Engineer Associate.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
Explain Conversational Artificial Intelligence Workloads Features on Azure (15-20%)
In the last section, you will face with the following subtopics:
- Establish Azure services for Conversational Artificial Intelligence – The potential candidates for the Microsoft AI-900 exam should have the capacity to identify the capabilities of Azure Bot Service and QnA Maker service.
- Identify the basic use cases for Conversational Artificial Intelligence (AI) – The students should be able to identify the features and usage for a range of elements. These include personal digital assistants, webchat bots, and telephone voice menus. It also covers the skills in identifying the basic features of conversational Artificial Intelligence solutions.
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Microsoft AI-900 中文 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Face - Identify types of computer vision solutions |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe capabilities of Azure Machine Learning - Describe machine learning pipelines - Describe automated machine learning - Describe core concepts of machine learning |
| Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe considerations for developing AI solutions - Describe responsible AI principles |
| Features of generative AI workloads on Azure | 20–25% | - Describe capabilities of Azure OpenAI Service - Describe generative AI concepts - Describe use cases for generative AI - Describe responsible AI practices for generative AI |
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Identify types of NLP solutions - Describe capabilities of Azure Language - Describe capabilities of Azure Speech - Describe capabilities of Azure Translator |
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