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The Microsoft AI-900 exam is the only qualifying test that leads to the Microsoft Certified: Azure AI Fundamentals certification. It measures one’s knowledge of AI considerations & workloads, features of NLP workloads on Azure, and basic principles of ML on Azure. The candidates can choose to take this exam in English, French, Simplified Chinese, Japanese, German, Spanish, or Korean. It contains 40-60 questions covering different types, including case studies, drag and drop, active screen, build list, and multiple choice. You can schedule your test with Certiport or Pearson VUE. However, it is required to pay the fee of $99 before scheduling it.

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Describe the Basic Principles of ML on Microsoft Azure (30-35%)

As for this area, it includes the following:

  • Describe the ML core concepts – You should be able to identify the labels and features within a dataset for ML and describe the usage of validation datasets and training in ML. The applicants also need the ability to explain the usage of ML algorithms in model training, as well as interpret and choose the model evaluation yardsticks for regression and classification.
  • Identify the major tasks in crafting an ML solution – This subject area measures the students’ competence in explaining some concepts, such as basic features of data preparation and ingestion, model management and deployment, model training & evaluation, and feature selection and engineering.
  • Identify the basic ML types – This section equips the learners with the ability to identify various concepts, such as regression, clustering, and classification ML scenarios.
  • Explain the abilities of the No-code ML with Azure ML Learning Studio – This domain equips the individuals with the knowledge of the Azure ML designer and automated ML UI.

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

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Microsoft AI-900日本語 Exam Syllabus Topics:

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

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