PDF Version DemoDifficulty in Attempting AI-900: Microsoft Azure AI Fundamentals Exam
Exam difficulty is not difficult and the material available in the Microsoft Learning website is sufficient to pass the exam easily with high score. The learning method needs to be fast. Otherwise, the score will not be high. The preparation material, including pre-exam certification, are used for time management. Use the exam simulator for practice. Performance requirements are quick response time. Translation of the data is required for this purpose. Updated and reliable information about the exam is required for this purpose. Real data is used for testing and certification. Engineering requirements are varied. Shopping website is used to buy the bag for this purpose. Microsoft AI-900 exam dumps are used in the exam. Relevant terminology used in AI-900:Microsoft Azure AI Fundamentals Exam is explained below. Metrics are used for this purpose. One of the metrics is accuracy. Basic tasks are performed for this purpose. Real-time analytics is used for this purpose. The architecture is required for the Microsoft Azure AI Fundamentals Exam.
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Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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-200: Azure Fundamentals Assessment
- AZ-100: Fundamentals of Azure
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Microsoft AI-900 中文 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Face - Describe capabilities of Azure Custom Vision - Identify types of computer vision solutions - Describe capabilities of Azure Form Recognizer |
| Topic 2: Artificial Intelligence workloads and considerations | 15–20% | - Describe responsible AI principles - Identify types of AI workloads - Describe considerations for developing AI solutions |
| Topic 3: Fundamental principles of machine learning on Azure | 15–20% | - Describe capabilities of Azure Machine Learning - Describe automated machine learning - Describe core concepts of machine learning - Describe machine learning pipelines |
| Topic 4: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Language - Identify types of NLP solutions - Describe capabilities of Azure Speech - Describe capabilities of Azure Translator |
| Topic 5: Features of generative AI workloads on Azure | 20–25% | - Describe capabilities of Azure OpenAI Service - Describe use cases for generative AI - Describe generative AI concepts - Describe responsible AI practices for generative AI |
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