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Introducing Updated Microsoft Certified: Azure AI Fundamentals Designation
This certificate is an opportunity for candidates to showcase their mastery of the Machine Learning (ML) and Artificial Intelligence (AI) workloads and their implementation of Azure services. For that reason, AI-900 exam can be taken by all candidates from technical and non-technical fields. While software engineering and data science skills may not be required for such an evaluation, it is important to have general programming knowledge to stand a chance of cracking this test right away. By and large, the updated Microsoft Certified: Azure AI Fundamentals designation can help you advance your career growth by preparing you for advanced Azure role-based certifications including the Microsoft Certified: Azure AI Engineer Associate and the Microsoft Certified: Azure Data Scientist Associate certificates.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
Average Salary of AI-900: Microsoft Azure AI Fundamentals Exam Certified Professionals
The average salary of an AI-900: Microsoft Azure AI Fundamentals Exam Outlook and Modules Exam Certified Expert in:
- Europe - 105,000 EURO
- United States - 155,000 USD
- England - 55,900 POUNDS
- India - 11,42,117 INR
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Describe NLP Workloads Features on Azure (15-20%)
This domain contains the following details that you need to learn about:
- Identify the features of basic NLP (Natural Language Processing) Workload Scenarios – The individuals should be able to identify various uses and features of various components, for example, keyphrase extraction, sentiment analysis, entity recognition, translation, language modeling, and speech recognition & synthesis.
- Identify Azure services & tools for Natural Language Processing Workloads – This topic is created to equip you with the ability to identify various capabilities, such as Speech service, Text Analytics service, Translator Text service, and Language Understanding service.
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Microsoft AI-900日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Describe AI workloads and considerations | 15-20% | - Identify features of common AI workloads - Identify guiding principles for responsible AI |
| Describe features of Generative AI workloads on Azure | 15-20% | - Identify responsible AI considerations for generative AI - Describe generative AI concepts - Describe Azure OpenAI Service capabilities |
| Describe features of computer vision workloads on Azure | 15-20% | - Identify Azure AI services for computer vision - Describe Azure capabilities for computer vision - Identify common computer vision tasks |
| Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify Azure AI services for NLP - Describe Azure capabilities for NLP - Identify common NLP tasks |
| Describe fundamental principles of machine learning on Azure | 30-35% | - Describe core machine learning concepts - Identify common machine learning tasks - Describe features of no-code automated ML - Describe Azure Machine Learning capabilities |
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