Microsoft AI-900 exam is an excellent certification for individuals who want to demonstrate their knowledge and skills in the field of artificial intelligence and machine learning. It provides a great starting point for individuals who want to pursue a career in this field and is also helpful for professionals who want to stay up-to-date with the latest technological advancements.
Skills measured
- Describe features of computer vision workloads on Azure (15-20%)
- Describe fundamental principles of machine learning on Azure (30-35%)
- Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)
- Describe features of conversational AI workloads on Azure (15-20%)
- Describe AI workloads and considerations (15-20%)
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The Microsoft AI-900 exam covers various topics such as Azure Cognitive Services, Azure Machine Learning, and other AI-related technologies. AI-900 exam is designed to test individuals' knowledge of AI concepts, machine learning algorithms, and data processing techniques. AI-900 exam also tests knowledge on how to implement AI solutions on Azure and how to use Azure tools and services for AI development.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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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 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.
- 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 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.
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Microsoft AI-900 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe AI workloads and considerations | 15-20% | - Identify features of common AI workloads - Identify guiding principles for responsible AI |
| Topic 2: Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify Azure AI services for NLP - Describe Azure capabilities for NLP - Identify common NLP tasks |
| Topic 3: Describe fundamental principles of machine learning on Azure | 30-35% | - Describe core machine learning concepts - Identify common machine learning tasks - Describe features of no-code automated ML - Describe Azure Machine Learning capabilities |
| Topic 4: Describe features of computer vision workloads on Azure | 15-20% | - Describe Azure capabilities for computer vision - Identify common computer vision tasks - Identify Azure AI services for computer vision |
| Topic 5: Describe features of Generative AI workloads on Azure | 15-20% | - Describe Azure OpenAI Service capabilities - Identify responsible AI considerations for generative AI - Describe generative AI concepts |






