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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:
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.
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.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe features of computer vision workloads on Azure | 15-20% | - Identify Azure AI services for computer vision - Identify common computer vision tasks - Describe Azure capabilities for computer vision |
| Topic 2: Describe fundamental principles of machine learning on Azure | 30-35% | - Describe features of no-code automated ML - Describe core machine learning concepts - Identify common machine learning tasks - Describe Azure Machine Learning capabilities |
| Topic 3: Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify Azure AI services for NLP - Identify common NLP tasks - Describe Azure capabilities for NLP |
| Topic 4: Describe AI workloads and considerations | 15-20% | - Identify features of common AI workloads - Identify guiding principles for responsible AI |
| Topic 5: 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 |
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