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The AI-900 exam covers various topics, including the fundamentals of AI, Azure AI services, computer vision, natural language processing, and conversational AI. AI-900 exam is designed to test your ability to work with Azure AI services, such as Azure Cognitive Services and Azure Machine Learning. You will also need to demonstrate your knowledge of key Azure tools that support AI and ML workloads, such as Azure Notebooks and Azure Databricks.
Requisites of Microsoft AI-900 Exam
In order to take the Microsoft AI-900 exam, you must have a current Azure subscription. You also need a valid Microsoft ID. In addition, be sure to pass the following exams:
- AZ-100: Fundamentals of Azure
- AZ-200: Azure Fundamentals Assessment
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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Microsoft AI-900 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Fundamental principles of machine learning on Azure | 15–20% | - Describe automated machine learning - Describe machine learning pipelines - Describe core concepts of machine learning - Describe capabilities of Azure Machine Learning |
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Language - Describe capabilities of Azure Speech - Identify types of NLP solutions - Describe capabilities of Azure Translator |
| Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Face - Identify types of computer vision solutions - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Computer Vision |
| Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Describe responsible AI principles - Identify types of AI workloads |
| Features of generative AI workloads on Azure | 20–25% | - Describe generative AI concepts - Describe capabilities of Azure OpenAI Service - Describe use cases for generative AI - Describe responsible AI practices for generative AI |
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