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Subjects of the test can be found on Microsoft Learning website. Clustering of data is one of the most important task on AI. It is used for improving accuracy. Labels are used to achieve this. Journey map is used to achieve this. Data grouping is used to achieve this. Validation is a process of finding what the probabilities are for the data to contain a given property. Remember to take the exam as early as possible.

Guides, strategies and testimonials you can find on the internet about the exam. Helpful to deal with the challenges in the exam. Attempt to use the free copies of the Microsoft Office Excel for this exam. Modeling approach for this exam is to use the AI technology through an artificial neural network.

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

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Microsoft AI-900 Certification Exam is an important credential for individuals who are looking to establish themselves as experts in the field of AI. It is recognized by a number of organizations and can be used to demonstrate an individual's knowledge and understanding of AI concepts and their applications. Additionally, individuals who hold this certification are more likely to be considered for career opportunities in the field of AI and data science.

Microsoft AI-900 Korean Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Features of Natural Language Processing (NLP) workloads on Azure15–20%- Describe capabilities of Azure Language
- Describe capabilities of Azure Speech
- Identify types of NLP solutions
- Describe capabilities of Azure Translator
Topic 2: Features of generative AI workloads on Azure20–25%- Describe use cases for generative AI
- Describe capabilities of Azure OpenAI Service
- Describe responsible AI practices for generative AI
- Describe generative AI concepts
Topic 3: Features of computer vision workloads on Azure15–20%- Identify types of computer vision solutions
- Describe capabilities of Azure Custom Vision
- Describe capabilities of Azure Face
- Describe capabilities of Azure Form Recognizer
- Describe capabilities of Azure Computer Vision
Topic 4: Fundamental principles of machine learning on Azure15–20%- Describe core concepts of machine learning
- Describe capabilities of Azure Machine Learning
- Describe machine learning pipelines
- Describe automated machine learning
Topic 5: Artificial Intelligence workloads and considerations15–20%- Identify types of AI workloads
- Describe considerations for developing AI solutions
- Describe responsible AI principles

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