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The Microsoft AI-900 exam, usually known as the Microsoft Azure AI Fundamentals, is well suited to baseline-level IT specialists with knowledge of artificial intelligence (AI) & Machine Learning (ML), and the relevant Microsoft Azure concepts. This is the only test that you should pass to qualify for the Microsoft Certified: Azure AI Fundamentals certification.

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Certification Path

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

Introducing Updated Microsoft Certified: Azure AI Fundamentals Designation

This certificate is an opportunity for candidates to showcase their mastery of the Machine Learning (ML) and Artificial Intelligence (AI) workloads and their implementation of Azure services. For that reason, AI-900 exam can be taken by all candidates from technical and non-technical fields. While software engineering and data science skills may not be required for such an evaluation, it is important to have general programming knowledge to stand a chance of cracking this test right away. By and large, the updated Microsoft Certified: Azure AI Fundamentals designation can help you advance your career growth by preparing you for advanced Azure role-based certifications including the Microsoft Certified: Azure AI Engineer Associate and the Microsoft Certified: Azure Data Scientist Associate certificates.

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Microsoft AI-900 中文 Exam Syllabus Topics:

SectionWeightObjectives
Describe features of computer vision workloads on Azure15-20%- Identify Azure AI services for computer vision
- Identify common computer vision tasks
- Describe Azure capabilities for computer vision
Describe fundamental principles of machine learning on Azure30-35%- Describe core machine learning concepts
- Identify common machine learning tasks
- Describe features of no-code automated ML
- Describe Azure Machine Learning capabilities
Describe features of Generative AI workloads on Azure15-20%- Describe generative AI concepts
- Identify responsible AI considerations for generative AI
- Describe Azure OpenAI Service capabilities
Describe features of Natural Language Processing (NLP) workloads on Azure15-20%- Identify Azure AI services for NLP
- Describe Azure capabilities for NLP
- Identify common NLP tasks
Describe AI workloads and considerations15-20%- Identify guiding principles for responsible AI
- Identify features of common AI workloads

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