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Microsoft AI-900, also known as the Microsoft Azure AI Fundamentals Exam, is a certification exam offered by Microsoft that assesses an individual's knowledge of artificial intelligence (AI) and machine learning (ML) concepts and their ability to apply them using Microsoft Azure technologies. AI-900-CN exam is intended for individuals who are interested in understanding AI and ML technologies and their applications in the Azure ecosystem. The AI-900 exam is considered an entry-level certification and is a great starting point for those who want to build a career in AI and ML.
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Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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To pass the Microsoft AI-900 exam, candidates must demonstrate their knowledge of various AI and machine learning concepts, including data analysis, natural language processing, and computer vision. Candidates must also have a good understanding of how these technologies can be used to solve real-world problems and how they can be implemented in Microsoft Azure. AI-900-CN exam consists of multiple-choice questions and is timed, with a total of 60 minutes to complete.
The AI-900 exam covers a wide range of topics related to AI and ML, including the basics of data science, machine learning, natural language processing, computer vision, and more. Candidates will be tested on their understanding of various Azure AI services, such as Azure Cognitive Services, Azure Machine Learning, and Azure Bot Service, and their ability to implement these services to develop intelligent solutions.
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe AI workloads and considerations | 15-20% | - Identify features of common AI workloads - Identify guiding principles for responsible AI |
| Topic 2: Describe features of computer vision workloads on Azure | 15-20% | - Describe Azure capabilities for computer vision - Identify common computer vision tasks - Identify Azure AI services for computer vision |
| Topic 3: Describe fundamental principles of machine learning on Azure | 30-35% | - Describe Azure Machine Learning capabilities - Describe features of no-code automated ML - Describe core machine learning concepts - Identify common machine learning tasks |
| Topic 4: Describe features of Generative AI workloads on Azure | 15-20% | - Describe Azure OpenAI Service capabilities - Describe generative AI concepts - Identify responsible AI considerations for generative AI |
| Topic 5: Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Describe Azure capabilities for NLP - Identify Azure AI services for NLP - Identify common NLP tasks |
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