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Training and Certification Guide
Microsoft Azure AI Fundamentals Exam Certification Details:
| Passing Score | 700 / 1000 |
| Number of Questions | 40-60 |
| Books / Training | Course AI-900T00: Microsoft Azure AI Fundamentals |
| Schedule Exam | Pearson VUE |
| Exam Price | $99 (USD) |
| Exam Name | Microsoft Certified - Azure AI Fundamentals |
| Sample Questions | Microsoft Azure AI Fundamentals Sample Questions |
| Duration | 60 mins |
| Exam Code | AI-900 |
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:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe fundamental principles of machine learning on Azure | 30-35% | - Identify common machine learning tasks - Describe features of no-code automated ML - Describe core machine learning concepts - Describe Azure Machine Learning capabilities |
| Topic 2: Describe features of Generative AI workloads on Azure | 15-20% | - Describe generative AI concepts - Describe Azure OpenAI Service capabilities - Identify responsible AI considerations for generative AI |
| Topic 3: 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 4: Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Describe Azure capabilities for NLP - Identify common NLP tasks - Identify Azure AI services for NLP |
| Topic 5: Describe AI workloads and considerations | 15-20% | - Identify guiding principles for responsible AI - Identify features of common AI workloads |
