Microsoft Data Engineering on Microsoft Azure (DP-203 Korean Version) : DP-203 Korean Exam

  • Exam Code: DP-203-KR
  • Exam Name: Data Engineering on Microsoft Azure (DP-203 Korean Version)
  • Updated: Aug 20, 2026
  • Q & A: 365 Questions and Answers

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About Microsoft Data Engineering on Microsoft Azure (DP-203 Korean Version) : DP-203 Korean Exam Questions

What is the salary of a Microsoft DP-203 certified professional?

The Average salary of different countries of Microsoft DP-203 Certified professional

  • United States - $104,000 USD

  • UK - 78707 Pounds

  • India - 7905404 INR

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Data Engineering on Microsoft Azure Exam Certification Details:

Passing Score700 / 1000
Number of Questions40-60
Exam NameMicrosoft Certified - Azure Data Engineer Associate
Duration150 mins
Books / TrainingDP-203T00: Data Engineering on Microsoft Azure
Exam Price$165 (USD)
Sample QuestionsData Engineering on Microsoft Azure Sample Questions
Schedule ExamPearson VUE
Exam CodeDP-203

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/dp-203

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Microsoft DP-203 Korean Exam Syllabus Topics:

SectionWeightObjectives
Design and Implement Data Security10-15%- Implement data security
  • 1. Manage sensitive data
  • 2. Configure authentication and authorization
  • 3. Implement data encryption
Design and Develop Data Processing25-30%- Develop stream processing solutions
  • 1. Process streaming data with Event Hubs
  • 2. Implement Azure Stream Analytics
- Develop batch processing solutions
  • 1. Develop Azure Data Factory pipelines
  • 2. Transform data using Apache Spark
  • 3. Implement Azure Databricks notebooks
Design and Implement Data Storage40-45%- Design a data storage structure
  • 1. Design a lakehouse architecture
  • 2. Design partitioning strategies
  • 3. Design a data warehouse schema
- Implement data storage solutions
  • 1. Implement Azure Synapse Analytics storage
  • 2. Implement Azure Data Lake Storage
  • 3. Implement Delta Lake
Monitor and Optimize Data Storage and Processing10-15%- Monitor data solutions
  • 1. Monitor Spark workloads
  • 2. Monitor pipeline execution
- Optimize performance
  • 1. Optimize data partitioning and indexing
  • 2. Optimize query performance

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