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Microsoft Azure AI Fundamentals (AI-900中文版): AI-900 Exam
"Microsoft Azure AI Fundamentals (AI-900中文版)", also known as AI-900 exam, is a Microsoft Certification. With the complete collection of questions and answers, PrepAwayTest has assembled to take you through 336 Q&As to your AI-900 Exam preparation. In the AI-900 exam resources, you will cover every field and category in Microsoft Certified: Azure AI Fundamentals Certification helping to ready you for your successful Microsoft Certification.
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Career Opportunities
The individuals can pursue a lot of career opportunities with a Microsoft certificate. The Microsoft Certified: Azure AI Fundamentals certification is not an exception. Although it is a foundational-level path, the certificate holders can start their careers on a good footing. Some of the job titles that these entry-level professionals can apply for include an Azure Data Scientist, an Azure AI Engineer, an Azure Administrator, an Azure Developer, an Azure Solution Architect, and an Azure DevOps Engineer. For these positions, the applicants can expect to earn an average of $80,000 per annum.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
Microsoft AI-900 Exam is an entry-level certification exam that does not require any prerequisites. However, candidates should have a basic understanding of cloud computing and Microsoft Azure. AI-900-CN exam consists of 40-60 multiple-choice questions, and candidates have 60 minutes to complete it. Upon passing the exam, candidates will receive the Microsoft Certified: Azure AI Fundamentals certification, which is a valuable credential in the AI industry.
Microsoft AI-900 中文 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe features of computer vision workloads on Azure | 15-20% | - Describe Azure capabilities for computer vision - Identify Azure AI services for computer vision - Identify common computer vision tasks |
| Topic 2: Describe fundamental principles of machine learning on Azure | 30-35% | - Identify common machine learning tasks - Describe Azure Machine Learning capabilities - Describe core machine learning concepts - Describe features of no-code automated ML |
| Topic 3: Describe AI workloads and considerations | 15-20% | - Identify features of common AI workloads - Identify guiding principles for responsible AI |
| Topic 4: Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify Azure AI services for NLP - Identify common NLP tasks - Describe Azure capabilities for NLP |
| Topic 5: 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 |
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