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The AI-900 certification is ideal for those who are new to the field of AI, as it covers the fundamentals of AI technologies and their applications. It is also suitable for professionals who work in other areas of technology, such as cloud computing and data analytics, and are looking to expand their knowledge.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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Preparing for the AI-900 exam is relatively easy, and Microsoft provides a range of learning resources to help you prepare for the exam, including online courses, study guides, and practice exams. AI-900-KR exam is available in several languages and is accessible online, making it convenient for individuals to take the exam from anywhere in the world.
Microsoft AI-900 certification exam is ideal for individuals who work with Azure cloud services or plan to migrate their applications to the Azure cloud. AI-900-KR exam is also suitable for IT professionals, developers, data analysts, and business analysts who want to gain a basic understanding of AI and its applications in the cloud environment. The AI-900 certification is a valuable addition to the resume of anyone interested in AI and machine learning.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Translator - Identify types of NLP solutions - Describe capabilities of Azure Language - Describe capabilities of Azure Speech |
| Topic 2: Features of computer vision workloads on Azure | 15–20% | - Identify types of computer vision solutions - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Face - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Form Recognizer |
| Topic 3: Fundamental principles of machine learning on Azure | 15–20% | - Describe core concepts of machine learning - Describe capabilities of Azure Machine Learning - Describe automated machine learning - Describe machine learning pipelines |
| Topic 4: Features of generative AI workloads on Azure | 20–25% | - Describe capabilities of Azure OpenAI Service - Describe generative AI concepts - Describe responsible AI practices for generative AI - Describe use cases for generative AI |
| Topic 5: Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Describe responsible AI principles - Identify types of AI workloads |
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