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Microsoft AI-900 certification exam, also known as the Microsoft Azure AI Fundamentals exam, is a fundamental-level exam that measures a candidate's knowledge and understanding of essential concepts related to artificial intelligence (AI) and machine learning (ML) on the Azure platform. Microsoft Azure AI Fundamentals (AI-900日本語版) certification exam is intended for individuals who wish to demonstrate their knowledge of AI and ML concepts and their practical applications using the Azure platform. AI-900J exam is designed to validate that the candidate has the foundational knowledge required to use Azure AI services to build intelligent solutions.

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Microsoft AI-900 (Microsoft Azure AI Fundamentals) Exam is a certification exam designed to test the fundamental knowledge of candidates in the field of artificial intelligence and machine learning. AI-900J exam is intended for individuals who want to validate their foundational knowledge of AI and its applications in the Microsoft Azure environment. AI-900J exam is suitable for technical and non-technical professionals who wish to gain knowledge and skills in AI and machine learning.

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900

Microsoft AI-900 Exam is an entry-level certification exam that does not require any prerequisites. However, candidates should have a basic understanding of cloud computing and Microsoft Azure. AI-900J exam consists of 40-60 multiple-choice questions, and candidates have 60 minutes to complete it. Upon passing the exam, candidates will receive the Microsoft Certified: Azure AI Fundamentals certification, which is a valuable credential in the AI industry.

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Microsoft AI-900日本語 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Features of computer vision workloads on Azure15–20%- Describe capabilities of Azure Computer Vision
- Describe capabilities of Azure Custom Vision
- Describe capabilities of Azure Form Recognizer
- Identify types of computer vision solutions
- Describe capabilities of Azure Face
Topic 2: Fundamental principles of machine learning on Azure15–20%- Describe machine learning pipelines
- Describe capabilities of Azure Machine Learning
- Describe core concepts of machine learning
- Describe automated machine learning
Topic 3: Artificial Intelligence workloads and considerations15–20%- Describe considerations for developing AI solutions
- Identify types of AI workloads
- Describe responsible AI principles
Topic 4: Features of generative AI workloads on Azure20–25%- Describe responsible AI practices for generative AI
- Describe capabilities of Azure OpenAI Service
- Describe use cases for generative AI
- Describe generative AI concepts
Topic 5: Features of Natural Language Processing (NLP) workloads on Azure15–20%- Describe capabilities of Azure Translator
- Describe capabilities of Azure Speech
- Describe capabilities of Azure Language
- Identify types of NLP solutions

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