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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.
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Learning Path
All in all, Microsoft provides two ways to help candidates prepare for the Microsoft AI-900 exam. These include the free online learning option and the paid, instructor-led training. Let’s discuss the two most popular courses offered by each of these options by the vendor:
- Microsoft Azure AI Fundamentals
This one-day course is part of the instructor-led learning platform and is, therefore, a paid option. As the name suggests, it is meant to introduce the candidates to fundamental AI concepts. The training, in particular, emphasizes the skills you need to become well versed in AI workloads and understand the Azure services that support such solutions. Usually, the course curriculum is viewed as comprehensive blended learning, featuring a combination of online materials and instructor-led training to ensure candidates have a good grasp of the exam topic areas. Even more, the hands-on exercises that are featured here are meant to help learners understand the test domains in real-world scenarios. Familiarity with the internet and computer technology is not a prerequisite but a great combination that will go a long way toward helping you register excellent results in this course and the ultimate exam.
- Get Started with Artificial Intelligence
The online gratis class, “Get Started with Artificial Intelligence” features one module and is intended to run for a maximum of 34 minutes. In general, this course is designed to help you get started with Microsoft services that empower new AI solutions. There are no prerequisites associated with this training. However, it features one module with a total of 10 units including the introduction and an understanding of machine learning, anomaly detection, computer vision, natural language processing, conversational AI, and responsible AI just to mention a few. Visit the official certification page to find out more information about this learning path that also includes classes on ML, NLP, and conversational AI.
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-100: Fundamentals of Azure
- AZ-200: Azure Fundamentals Assessment
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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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.
Microsoft AI-900 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Features of generative AI workloads on Azure | 20–25% | - Describe responsible AI practices for generative AI - Describe generative AI concepts - Describe use cases for generative AI - Describe capabilities of Azure OpenAI Service |
| Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Face - Identify types of computer vision solutions - Describe capabilities of Azure Form Recognizer |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe capabilities of Azure Machine Learning - Describe core concepts of machine learning - Describe automated machine learning - Describe machine learning pipelines |
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Language - Describe capabilities of Azure Translator - Describe capabilities of Azure Speech - Identify types of NLP solutions |
| Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe considerations for developing AI solutions - Describe responsible AI principles |




