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Requisites of Microsoft AI-900 Exam
In order to take the Microsoft AI-900 exam, you must have a current Azure subscription. You also need a valid Microsoft ID. In addition, be sure to pass the following exams:
- AZ-100: Fundamentals of Azure
- AZ-200: Azure Fundamentals Assessment
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
Microsoft AI-900 Practice Test Questions, Microsoft AI-900 Exam Practice Test Questions
The individuals who passed the Microsoft AI-900 exam possess the foundational knowledge of artificial intelligence (AI) and machine learning (ML). The test is designed to evaluate the candidates’ understanding of basic AI and ML workloads as well as how to execute them on Microsoft Azure. The students can earn the Microsoft Certified: Azure AI Fundamentals certification at the end of their journey with this path.
Microsoft AI-900 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of generative AI workloads on Azure | 20–25% | - Describe capabilities of Azure OpenAI Service - Describe use cases for generative AI - Describe responsible AI practices for generative AI - Describe generative AI concepts |
| Topic 2: Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe considerations for developing AI solutions - Describe responsible AI principles |
| Topic 3: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Language - Describe capabilities of Azure Translator - Identify types of NLP solutions - Describe capabilities of Azure Speech |
| Topic 4: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Face - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Custom Vision - Identify types of computer vision solutions |
| Topic 5: Fundamental principles of machine learning on Azure | 15–20% | - Describe core concepts of machine learning - Describe machine learning pipelines - Describe automated machine learning - Describe capabilities of Azure Machine Learning |
