Microsoft AI-900 certification exam provides a solid foundation for individuals who want to develop their skills in artificial intelligence and explore its applications in the Azure platform. By earning this certification, candidates can demonstrate their expertise to potential employers and open up opportunities for career advancement in the rapidly growing field of AI.
Microsoft AI-900 certification exam covers a range of topics related to AI, including machine learning, natural language processing, computer vision, and cognitive services. AI-900 exam is designed to test the candidate's understanding of the core concepts of AI and how they can be applied to real-world scenarios. Candidates who pass the exam demonstrate their ability to use Microsoft Azure AI services to build intelligent solutions.
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 |
|---|---|---|
| Topic 1: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Speech - Describe capabilities of Azure Language - Describe capabilities of Azure Translator - Identify types of NLP solutions |
| Topic 2: Features of generative AI workloads on Azure | 20–25% | - Describe capabilities of Azure OpenAI Service - Describe use cases for generative AI - Describe generative AI concepts - Describe responsible AI practices for generative AI |
| Topic 3: Features of computer vision workloads on Azure | 15–20% | - Identify types of computer vision solutions - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Face |
| Topic 4: Artificial Intelligence workloads and considerations | 15–20% | - Describe responsible AI principles - Identify types of AI workloads - Describe considerations for developing AI solutions |
| Topic 5: Fundamental principles of machine learning on Azure | 15–20% | - Describe machine learning pipelines - Describe automated machine learning - Describe capabilities of Azure Machine Learning - Describe core concepts of machine learning |
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