Microsoft AI-900 exam covers a wide range of topics related to AI and machine learning, including machine learning workloads, computer vision workloads, natural language processing workloads, and conversational AI workloads. Candidates will be tested on their understanding of the key concepts and terminology related to these topics, as well as their ability to use the Azure AI services to build intelligent solutions.
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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.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
Microsoft AI-900 exam, also known as the Microsoft Azure AI Fundamentals exam, is an entry-level certification exam that focuses on the basics of artificial intelligence and machine learning. AI-900J exam is designed for individuals who want to demonstrate their knowledge of AI and machine learning concepts and how these technologies can be used in Microsoft Azure. AI-900J exam covers a wide range of topics, including machine learning basics, cognitive services, and bot services.
Microsoft AI-900日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Fundamental principles of machine learning on Azure | 15–20% | - Describe core concepts of machine learning - Describe machine learning pipelines - Describe capabilities of Azure Machine Learning - Describe automated machine learning |
| Topic 2: 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 3: Features of generative AI workloads on Azure | 20–25% | - Describe responsible AI practices for generative AI - Describe use cases for generative AI - Describe generative AI concepts - Describe capabilities of Azure OpenAI Service |
| Topic 4: Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Describe responsible AI principles - Identify types of AI workloads |
| Topic 5: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Face - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Form Recognizer - Identify types of computer vision solutions |






