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The Microsoft AI-900 exam will measure the candidates’ skills and competence in a range of topics. They are as follows:
- Explain the Features of Computer Vision Workloads Available on Azure (15-20%): This domain requires that the test takers demonstrate competence in identifying the basic categories of computer vision solutions. It will also measure their skills in identifying different Azure services and tools for computer vision tasks. You will also need an understanding of the capabilities of Computer Vision service, Custom Vision service, Face service, and Form Recognizer service.
- Explain AI Workloads & Considerations (15-20%): This section will measure the individuals’ ability to identify different features of common artificial intelligence workloads. It will also evaluate their competence in identifying the guiding principles that are responsible for AI.
- Explain the Features of NLP (Natural Language Processing) Workloads Available on Azure (15-20%): This subject area will measure your ability to identify the features of basic Natural Language Processing Workload scenarios. It will also test your skills in identifying different Azure services and tools for NLP workloads. The topic will cover the understanding of the capabilities of Text Analytics service, Language Understanding service, Speech service, and Translator Text service.
- Explain the Features of Conversational Artificial Intelligence Workloads Available on Azure (15-20%): The applicants must demonstrate the understanding of common use cases associated with conversational artificial intelligence. This area also measures one’s knowledge of Azure services associated with conversational artificial intelligence.
- Explain the Fundamental Principles of ML on Azure (30-35%): The potential candidates for the Microsoft AI-900 exam should be able to identify the common types of machine learning and explain its core concepts. They also need to know how to identify the core tasks that are involved in creating the ML solutions. Additionally, they need to have the knowledge of the capabilities of no-code ML with Azure ML studio.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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Career Prospects
Although the Microsoft AI-900 exam is associated with the foundational-level certification, all the successful candidates can pursue various lucrative career paths in the field of IT. Some of the job roles that these professionals can work in include a Data Scientist, a Software Developer, and an Azure AI Engineer, among others. They can also proceed to get some other role-based certificates to boost their potential in the world of Information Technology. With the Microsoft Certified: Azure AI Fundamentals certification, you can earn an average of $48,000 per annum as an entry-level specialist. Those with some work experience can get an average of $74,000 per year.
Exam Overview
This certification test is available in English, Spanish, Simplified Chinese, Japanese, French, Korean, and German. You will find different formats of questions while dealing with this Microsoft exam. These include multiple choice, drag and drop, build list, active screen, short answer, and best answer. The test costs $99 and the learners can register for it through Pearson VUE or Certiport.
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Requisites of Microsoft AI-900 Exam
In order to take the Microsoft AI-900 exam, you must have a current Azure subscription. You also need a valid Microsoft ID. In addition, be sure to pass the following exams:
- AZ-200: Azure Fundamentals Assessment
- AZ-100: Fundamentals of Azure
Microsoft AI-900 中文 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| 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 |
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Speech - Describe capabilities of Azure Language - Identify types of NLP solutions - Describe capabilities of Azure Translator |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe automated machine learning - Describe capabilities of Azure Machine Learning - Describe machine learning pipelines - Describe core concepts of machine learning |
| Artificial Intelligence workloads and considerations | 15–20% | - Describe responsible AI principles - Describe considerations for developing AI solutions - Identify types of AI workloads |
| Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Custom Vision - Identify types of computer vision solutions - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Face |

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