Certification Topics of Microsoft AI-900 Exam
Our Microsoft AI-900 exam dumps covers the following objectives of Microsoft AI-900 Exam.
- Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)
- Describe AI workloads and considerations (15-20%)
- Describe fundamental principles of machine learning on Azure (30-35%)
- Describe features of conversational AI workloads on Azure (15-20%)
- Describe features of computer vision workloads on Azure (15-20%)
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Target Audience
The Microsoft AI-900 exam is designed for those individuals who have little to no experience in the world of IT. It is aimed at the students with both non-technical and technical backgrounds. They have basic programming experience and knowledge. However, they are not required to have software engineering or data science experience.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
Microsoft AI-900 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Speech - Describe capabilities of Azure Translator - Describe capabilities of Azure Language - Identify types of NLP solutions |
| Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Custom Vision - Identify types of computer vision solutions - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Face |
| Artificial Intelligence workloads and considerations | 15–20% | - Describe responsible AI principles - Identify types of AI workloads - Describe considerations for developing AI solutions |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe capabilities of Azure Machine Learning - Describe automated machine learning - Describe machine learning pipelines - Describe core concepts of machine learning |
| Features of generative AI workloads on Azure | 20–25% | - Describe capabilities of Azure OpenAI Service - Describe use cases for generative AI - Describe responsible AI practices for generative AI - Describe generative AI concepts |
