Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100 Korean Version) : DP-100 Korean

  • Exam Code: DP-100 Korean
  • Exam Name: Designing and Implementing a Data Science Solution on Azure (DP-100 Korean Version)
  • Updated: Aug 17, 2026
  • Q & A: 528 Questions and Answers

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About Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100 Korean Version) : DP-100 Korean Exam

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Microsoft DP-100: Requirements

The Microsoft DP-100 exam is created for the individuals who are involved in implementing machine learning techniques. The candidates for this certification test should have an in-depth knowledge of identifying and preparing the development environment, applying scientific data and rigor exploration techniques to acquire actionable insights along with communicating results to the stakeholders. They should also possess the skills in preparing data for modeling and developing models. In addition, they should also be able to apply machine learning techniques to train, deploy and evaluate models to build any AI solutions, which satisfy the business objectives.

Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx

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Career Opportunities & Salary Outlook

The demand for the certified professionals is on the increase. As an Azure Data Scientist, you can explore many employment opportunities. So, if you want to boost your career potential, pursuing the Microsoft Certified: Azure Data Scientist Associate certification is the right step. To get this certificate, you have to pass the qualifying exam, which is Microsoft DP-100. With this associate-level certification, you can get the positions, such as a Data Scientist, a Senior Data Scientist, a Data Science Manager, and a Data Science Director. The average salary for these job roles is $135,000 per annum. With advanced experience, the specialists can earn up to $170,000 per year.

Microsoft DP-100 Korean Exam Syllabus Topics:

SectionObjectives
Topic 1: Optimize and manage models- Improve model performance
- Track experiments and manage model lifecycle
Topic 2: Deploy and consume models- Monitor deployed models and endpoints
- Deploy models to endpoints
Topic 3: Explore and analyze data- Ingest and prepare data for modeling
- Perform exploratory data analysis
Topic 4: Design and prepare a machine learning solution- Plan and configure Azure Machine Learning workspace
- Manage compute and data assets
- Select appropriate Azure services for machine learning workloads
Topic 5: Train machine learning models- Train models using Azure Machine Learning
- Tune hyperparameters and evaluate models

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