- Exam Code: AI-900
- Exam Name: Microsoft Azure AI Fundamentals (AI-900 Korean Version)
- Certification Provider: Microsoft
- Corresponding Certification:Microsoft Certified: Azure AI Fundamentals
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Instant Download Microsoft : AI-900 Korean Questions & Answers as PDF & Test Engine
- Exam Code: AI-900
- Exam Name: Microsoft Azure AI Fundamentals (AI-900 Korean Version)
- Updated: Jul 21, 2026
- No. of Questions: 336 Questions and Answers
- Download Limit: Unlimited
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Although the Microsoft AI-900 exam is associated with the foundational-level certification, all the successful candidates can pursue various lucrative career paths in the field of IT. Some of the job roles that these professionals can work in include a Data Scientist, a Software Developer, and an Azure AI Engineer, among others. They can also proceed to get some other role-based certificates to boost their potential in the world of Information Technology. With the Microsoft Certified: Azure AI Fundamentals certification, you can earn an average of $48,000 per annum as an entry-level specialist. Those with some work experience can get an average of $74,000 per year.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
For more info read reference:
Microsoft AI-900 Official Certification Learning Site
Training and Certification Guide
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Microsoft AI-900 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe AI workloads and considerations | 15-20% | - Identify guiding principles for responsible AI - Identify features of common AI workloads |
| Topic 2: Describe features of computer vision workloads on Azure | 15-20% | - Identify common computer vision tasks - Describe Azure capabilities for computer vision - Identify Azure AI services for computer vision |
| Topic 3: Describe features of Generative AI workloads on Azure | 15-20% | - Identify responsible AI considerations for generative AI - Describe generative AI concepts - Describe Azure OpenAI Service capabilities |
| Topic 4: Describe fundamental principles of machine learning on Azure | 30-35% | - Describe features of no-code automated ML - Describe core machine learning concepts - Identify common machine learning tasks - Describe Azure Machine Learning capabilities |
| Topic 5: Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify common NLP tasks - Identify Azure AI services for NLP - Describe Azure capabilities for NLP |
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