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The technologies that are tested on Microsoft AI-900 exam are necessary to develop intelligent operations based on cloud computing. Audience for this certification exam is individuals who work as computer programmers, developers, system administrators and project managers. Built for high-volume, high-speed web applications that use Azure, this Examines the skills required to develop intelligent cloud solutions. Smarter IT environments are the goal of this exam. Single sign-on for Azure, is an exam objective. Microsoft AI-900 exam dumps helps to make it easier for students to get this certification. Outline the business value of the exam. The importance of having this certification. Identify the technologies covered on Microsoft AI-900 exam. Gain a better understanding of the skills you'll need to develop intelligent cloud solutions.
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Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
Microsoft AI-900 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Translator - Describe capabilities of Azure Language - Describe capabilities of Azure Speech - Identify types of NLP solutions |
| Topic 2: Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Identify types of AI workloads - Describe responsible AI principles |
| Topic 3: Fundamental principles of machine learning on Azure | 15–20% | - Describe capabilities of Azure Machine Learning - Describe core concepts of machine learning - Describe automated machine learning - Describe machine learning pipelines |
| Topic 4: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Face - Identify types of computer vision solutions - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Computer Vision |
| Topic 5: Features of generative AI workloads on Azure | 20–25% | - Describe capabilities of Azure OpenAI Service - Describe use cases for generative AI - Describe generative AI concepts - Describe responsible AI practices for generative AI |
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