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Candidates for this exam should have foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.
This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.
This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, some general programming knowledge or experience would be beneficial.
Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them.
Part of the requirements for: Microsoft Certified: Azure AI Fundamentals
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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The Microsoft Azure AI Fundamentals (AI-900中文版) certification exam consists of 40-60 multiple choice questions that candidates must complete within 85 minutes. AI-900-CN exam can be taken in-person at a testing center or online from the comfort of your own home. To prepare for the exam, Microsoft offers a range of study materials, including online training courses, practice exams, and study guides. Additionally, candidates can also find third-party study materials and practice exams online.
The Microsoft Azure AI Fundamentals (AI-900中文版) certification exam is designed for professionals across a range of industries, including software developers, data scientists, business analysts, and IT professionals. Whether you are looking to advance your career or gain new skills, the AI-900 certification exam provides a great opportunity to demonstrate your expertise in AI and machine learning.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of generative AI workloads on Azure | 20–25% | - Describe generative AI concepts - Describe responsible AI practices for generative AI - Describe use cases for generative AI - Describe capabilities of Azure OpenAI Service |
| Topic 2: 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 3: Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Describe responsible AI principles - Identify types of AI workloads |
| Topic 4: 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 5: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Computer Vision - Identify types of computer vision solutions - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Face |
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