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About Azure AI Fundamentals Certification and Who It Is Intended for
The Microsoft Certified: Azure AI Fundamentals qualification authenticates the learner's understanding and skills in AI & ML concepts along with associated Azure services. In particular, the individual with this certification is competent in demonstrating their understanding of various workloads related to those terms along with their implementation in Azure cloud computing services. In addition, this certification is an excellent way to get a step ahead in the preparation for other role-based Azure certificates as well as showcasing one's skill in the fundamentals of ML as well as AI. All in all, the Azure AI Fundamentals validation form Microsoft is for beginners and thus, doesn't require any technical knowledge and expertise like, for example, engineering for software. However, some understanding of programming will help you a lot to easily nail test AI-900.
Explain Conversational Artificial Intelligence Workloads Features on Azure (15-20%)
In the last section, you will face with the following subtopics:
- Identify the basic use cases for Conversational Artificial Intelligence (AI) – The students should be able to identify the features and usage for a range of elements. These include personal digital assistants, webchat bots, and telephone voice menus. It also covers the skills in identifying the basic features of conversational Artificial Intelligence solutions.
- Establish Azure services for Conversational Artificial Intelligence – The potential candidates for the Microsoft AI-900 exam should have the capacity to identify the capabilities of Azure Bot Service and QnA Maker service.
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Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
Microsoft AI-900 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| 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 Form Recognizer - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Face |
| 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 |
| Features of generative AI workloads on Azure | 20–25% | - Describe use cases for generative AI - Describe capabilities of Azure OpenAI Service - Describe responsible AI practices for generative AI - Describe generative AI concepts |
| 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 machine learning pipelines - Describe capabilities of Azure Machine Learning - Describe automated machine learning - Describe core concepts of machine learning |
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