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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
The potential candidates for this certification test must develop their skills in its topics before attempting it. It is recommended to go through the official webpage to review the detailed objectives. Microsoft updates the topics of its exams regularly, so studying the latest domains is very important to your success in the test. Thus, Microsoft AI-900 covers the following sections:
Describe AI Considerations and Workloads (15-20%)
The first topic evaluates the following skills:
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For people working in technology, having an Azure certification becomes important. The high demand for Azure certified experts, the high salary and benefits. Microsoft Azure AI Fundamentals Exam Certification give you skills and knowledge about a particular area of technology.
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Subjects of the test can be found on Microsoft Learning website. Clustering of data is one of the most important task on AI. It is used for improving accuracy. Labels are used to achieve this. Journey map is used to achieve this. Data grouping is used to achieve this. Validation is a process of finding what the probabilities are for the data to contain a given property. Remember to take the exam as early as possible.
Guides, strategies and testimonials you can find on the internet about the exam. Helpful to deal with the challenges in the exam. Attempt to use the free copies of the Microsoft Office Excel for this exam. Modeling approach for this exam is to use the AI technology through an artificial neural network.
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| Section | Weight | Objectives |
|---|---|---|
| Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Computer Vision - Identify types of computer vision solutions - Describe capabilities of Azure Face |
| Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe considerations for developing AI solutions - Describe responsible AI principles |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe core concepts of machine learning - Describe automated machine learning - Describe machine learning pipelines - Describe capabilities of Azure Machine Learning |
| Features of generative AI workloads on Azure | 20–25% | - Describe capabilities of Azure OpenAI Service - Describe use cases for generative AI - 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 Translator - Describe capabilities of Azure Language - Describe capabilities of Azure Speech - Identify types of NLP solutions |
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