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Microsoft AI-900 exam is an excellent way to demonstrate your knowledge and skills in the field of artificial intelligence and machine learning. Microsoft Azure AI Fundamentals (AI-900中文版) certification is useful for professionals who want to pursue a career in AI or machine learning or for those who want to expand their knowledge and skills in this area. Microsoft Azure AI Fundamentals (AI-900中文版) certification is also helpful for individuals who work in the IT industry and want to stay up-to-date with the latest technological advancements.
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Microsoft AI-900 exam is an entry-level certification exam offered by Microsoft to validate knowledge and understanding of the fundamental concepts of Artificial Intelligence (AI) and its implementation on the Azure platform. AI-900-CN exam is ideal for individuals who are new to the field of AI and want to showcase their expertise in this area.
Microsoft AI-900 certification provides individuals with a strong foundation in the basics of AI and Azure, which can be useful in various roles, such as data analysts, developers, or project managers. Microsoft Azure AI Fundamentals (AI-900中文版) certification also serves as a prerequisite for other advanced Azure certifications. Passing the exam requires a score of 700 or higher out of a possible 1000 points.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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Certification Topics of Microsoft AI-900 Exam
Our Microsoft AI-900 exam dumps covers the following objectives of Microsoft AI-900 Exam.
- Describe features of computer vision workloads on Azure (15-20%)
- Describe fundamental principles of machine learning on Azure (30-35%)
- Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)
- Describe features of conversational AI workloads on Azure (15-20%)
- Describe AI workloads and considerations (15-20%)
Microsoft AI-900 中文 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Describe features of computer vision workloads on Azure | 15-20% | - Identify Azure AI services for computer vision - Identify common computer vision tasks - Describe Azure capabilities for computer vision |
| Describe AI workloads and considerations | 15-20% | - Identify guiding principles for responsible AI - Identify features of common AI workloads |
| Describe features of Generative AI workloads on Azure | 15-20% | - Describe Azure OpenAI Service capabilities - Identify responsible AI considerations for generative AI - Describe generative AI concepts |
| Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify Azure AI services for NLP - Describe Azure capabilities for NLP - Identify common NLP tasks |
| Describe fundamental principles of machine learning on Azure | 30-35% | - Describe Azure Machine Learning capabilities - Describe core machine learning concepts - Identify common machine learning tasks - Describe features of no-code automated ML |
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