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Microsoft AI-900 中文 Practice Q&A's

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Career Opportunities

The individuals can pursue a lot of career opportunities with a Microsoft certificate. The Microsoft Certified: Azure AI Fundamentals certification is not an exception. Although it is a foundational-level path, the certificate holders can start their careers on a good footing. Some of the job titles that these entry-level professionals can apply for include an Azure Data Scientist, an Azure AI Engineer, an Azure Administrator, an Azure Developer, an Azure Solution Architect, and an Azure DevOps Engineer. For these positions, the applicants can expect to earn an average of $80,000 per annum.

Target Audience

The Microsoft AI-900 exam is designed for those individuals who have little to no experience in the world of IT. It is aimed at the students with both non-technical and technical backgrounds. They have basic programming experience and knowledge. However, they are not required to have software engineering or data science experience.

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 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 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 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 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.

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900

For more info read reference:

Microsoft AI-900 Official Certification Learning Site

Training and Certification Guide

Exam policies and FAQs

Microsoft AI-900 中文 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Describe fundamental principles of machine learning on Azure30-35%- Describe features of no-code automated ML
- Describe Azure Machine Learning capabilities
- Describe core machine learning concepts
- Identify common machine learning tasks
Topic 2: Describe AI workloads and considerations15-20%- Identify guiding principles for responsible AI
- Identify features of common AI workloads
Topic 3: Describe features of Natural Language Processing (NLP) workloads on Azure15-20%- Identify Azure AI services for NLP
- Describe Azure capabilities for NLP
- Identify common NLP tasks
Topic 4: Describe features of computer vision workloads on Azure15-20%- Identify Azure AI services for computer vision
- Identify common computer vision tasks
- Describe Azure capabilities for computer vision
Topic 5: Describe features of Generative AI workloads on Azure15-20%- Describe Azure OpenAI Service capabilities
- Describe generative AI concepts
- Identify responsible AI considerations for generative AI

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