Microsoft AI-900 Korean - Microsoft Azure AI Fundamentals (AI-900 Korean Version)

Microsoft AI-900 Korean Actual PDF
  • Exam Code: AI-900-KR
  • Exam Name: Microsoft Azure AI Fundamentals (AI-900 Korean Version)
  • Updated: Aug 28, 2026
  • Q & A: 336 Questions and Answers
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Prerequisites

This certification test has no official prerequisites. However, the interested candidates must develop their skills and knowledge in the domains of the exam topics. Although the individuals do not need this test to pursue more advanced Azure role-based options, they can gain extensive expertise while preparing for this exam. Your knowledge base can contribute to the success of more advanced certificates such as Microsoft Certified: Azure AI Engineer Associate or Microsoft Certified: Azure Data Scientist 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 Korean Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Describe fundamental principles of machine learning on Azure30-35%- Describe core machine learning concepts
- Describe Azure Machine Learning capabilities
- Identify common machine learning tasks
- Describe features of no-code automated ML
Topic 2: Describe features of Natural Language Processing (NLP) workloads on Azure15-20%- Identify common NLP tasks
- Identify Azure AI services for NLP
- Describe Azure capabilities for NLP
Topic 3: Describe AI workloads and considerations15-20%- Identify guiding principles for responsible AI
- Identify features of common AI workloads
Topic 4: Describe features of computer vision workloads on Azure15-20%- Describe Azure capabilities for computer vision
- Identify Azure AI services for computer vision
- Identify common computer vision tasks
Topic 5: Describe features of Generative AI workloads on Azure15-20%- Identify responsible AI considerations for generative AI
- Describe Azure OpenAI Service capabilities
- Describe generative AI concepts

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