About Exam AI-900
The interested candidate for the Microsoft AI-900 test, which also goes under the name the Azure test for AI Fundamentals, should have at least some foundational understanding of the concept of AI features alongside ML ones. This exam will test the entrant's knowledge of different AI common workloads, principles of AI and ML, NLP workloads, and other related concepts. When it comes to the details for this exam, the Microsoft AI-900 is available in several languages, among which you'll find Spanish, English, Korean, German, Chinese (Simplified), and French. Such an exam comes with a registration fee of $99 and should be scheduled through Pearson VUE. Also, in the main exam, candidates will be exposed to around 40-60 tasks with 1 hour given to solve them.
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Explain Computer Vision Workloads Features on Azure (15-20%)
Here, the following skills will be measured:
- Identify the basic types of Computer Vision Solution – The candidates need to understand and be able to identify a range of features. They include image classification solutions, semantic segmentation solutions, object detection solutions, and optical attributes recognition solutions. It also covers the attributes of facial recognition, facial analysis, and facial detection solutions.
- Identify Azure services & tools for Computer Vision Projects – This section requires that the learners identify different capabilities of Computer Vision service, Face service, Customer Vision service, and Form Recognizer service.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
What is the duration, language, and format of AI-900: Microsoft Azure AI Fundamentals Exam
- Length of Examination: 50 mins
- Number of Questions: 100 to 120 questions(Since Microsoft does not publish this information, the number of exam questions may change without notice)
- Type of Questions: This test format is multiple choice
- Languages in which this exam is available in: English, Japanese, Chinese (Simplified), Korean, German, French, Spanish
- Passing Score: 70%
Explain Conversational Artificial Intelligence Workloads Features on Azure (15-20%)
In the last section, you will face with the following subtopics:
- Identify the basic use cases for Conversational Artificial Intelligence (AI) – The students should be able to identify the features and usage for a range of elements. These include personal digital assistants, webchat bots, and telephone voice menus. It also covers the skills in identifying the basic features of conversational Artificial Intelligence solutions.
- Establish Azure services for Conversational Artificial Intelligence – The potential candidates for the Microsoft AI-900 exam should have the capacity to identify the capabilities of Azure Bot Service and QnA Maker service.
Microsoft AI-900 中文 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of generative AI workloads on Azure | 20–25% | - Describe use cases for generative AI - Describe generative AI concepts - Describe responsible AI practices for generative AI - Describe capabilities of Azure OpenAI Service |
| Topic 2: Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Identify types of AI workloads - Describe responsible AI principles |
| Topic 3: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Language - Describe capabilities of Azure Speech - Identify types of NLP solutions - Describe capabilities of Azure Translator |
| Topic 4: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Face - Identify types of computer vision solutions - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Computer Vision |
| Topic 5: 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 |
