PDF Version DemoExam Topics
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:
- Identify the guiding principles associated with responsible Artificial Intelligence – This one will evaluate the candidates’ ability to explain some concepts. These include considerations of safety and reliability in AI solutions and considerations for fairness in AI solutions. It also covers considerations for security and privacy, transparency, inclusiveness, and accountability within an AI solution.
- Identify the common AI Workloads features – You should be able to identify a variety of workloads, such as forecasting/prediction, computer vision, and conversational AI. The subsection also covers the characteristics of anomaly detection workloads, knowledge mining, and natural language processing workloads.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
Prerequisites
This certification test has no official prerequisites. However, the interested candidates must develop their skills and knowledge in the domains of the exam topics. Although the individuals do not need this test to pursue more advanced Azure role-based options, they can gain extensive expertise while preparing for this exam. Your knowledge base can contribute to the success of more advanced certificates such as Microsoft Certified: Azure AI Engineer Associate or Microsoft Certified: Azure Data Scientist Associate.
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Microsoft AI-900 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Identify types of NLP solutions - Describe capabilities of Azure Language - Describe capabilities of Azure Speech - Describe capabilities of Azure Translator |
| Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Identify types of AI workloads - Describe responsible AI principles |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe core concepts of machine learning - Describe capabilities of Azure Machine Learning - Describe machine learning pipelines - Describe automated machine learning |
| Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Computer Vision - Identify types of computer vision solutions - Describe capabilities of Azure Face - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Custom Vision |
| Features of generative AI workloads on Azure | 20–25% | - Describe generative AI concepts - Describe use cases for generative AI - Describe responsible AI practices for generative AI - Describe capabilities of Azure OpenAI Service |
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