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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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It's important to note that the Microsoft AI-900 exam isn't an exam on Azure; the objective of the exam is to make sure you know how to build and manage intelligent cloud solutions and services on Azure. Foundation to Azure, or you can still get the certificate. Defined best practices for solution design, deployment, and administration. Identity of Azure services. Conclude testing by connecting the solution to the infrastructure to test for functionality. Instructor for the exam has extensive experience with Azure. Textidentify Azure services. Define identity of Azure services. Rewarding in the form of free exam vouchers and in-demand credential. Microsoft AI-900 exam dumps prepare you to become qualified Azure solutions specialist. Focus on the skills that are of high value in your organization. Bots are an important part of this exam.

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Microsoft AI-900日本語 Exam Syllabus Topics:

SectionWeightObjectives
Features of computer vision workloads on Azure15–20%- Describe capabilities of Azure Face
- Describe capabilities of Azure Form Recognizer
- Describe capabilities of Azure Custom Vision
- Describe capabilities of Azure Computer Vision
- Identify types of computer vision solutions
Features of generative AI workloads on Azure20–25%- Describe capabilities of Azure OpenAI Service
- Describe generative AI concepts
- Describe responsible AI practices for generative AI
- Describe use cases for generative AI
Fundamental principles of machine learning on Azure15–20%- Describe automated machine learning
- Describe capabilities of Azure Machine Learning
- Describe machine learning pipelines
- Describe core concepts of machine learning
Artificial Intelligence workloads and considerations15–20%- Identify types of AI workloads
- Describe responsible AI principles
- Describe considerations for developing AI solutions
Features of Natural Language Processing (NLP) workloads on Azure15–20%- Describe capabilities of Azure Translator
- Identify types of NLP solutions
- Describe capabilities of Azure Speech
- Describe capabilities of Azure Language
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