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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
Describe NLP Workloads Features on Azure (15-20%)
This domain contains the following details that you need to learn about:
- 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.
- 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.
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Career Opportunities
As you remember, the Microsoft Certified: Azure AI Fundamentals validation is the fundamental certification designed for beginners with both technical and non-technical backgrounds. It is considered a good path to follow if you're interested in starting a career in the field of AI and ML. Below are some of the jobs that you can get your hand on through this certification. These jobs come with decent annual salaries as covered by Payscale.com:
- AI Engineer: $76k;
- Data Scientist: $67k.
- Developer: $50k;
Microsoft AI-900 中文 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of generative AI workloads on Azure | 20–25% | - Describe use cases for generative AI - Describe capabilities of Azure OpenAI Service - Describe generative AI concepts - Describe responsible AI practices for generative AI |
| Topic 2: Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe considerations for developing AI solutions - Describe responsible AI principles |
| Topic 3: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Identify types of NLP solutions - Describe capabilities of Azure Translator - Describe capabilities of Azure Language - Describe capabilities of Azure Speech |
| Topic 4: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Face - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Custom Vision - Identify types of computer vision solutions - Describe capabilities of Azure Computer Vision |
| Topic 5: Fundamental principles of machine learning on Azure | 15–20% | - Describe automated machine learning - Describe capabilities of Azure Machine Learning - Describe core concepts of machine learning - Describe machine learning pipelines |
