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DP-203 Korean
  • Exam Code: DP-203
  • Exam Name: Data Engineering on Microsoft Azure (DP-203 Korean Version)
  • Updated: Jul 31, 2026
  • No. of Questions: 365 Questions and Answers
  • Download Limit: Unlimited
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How to Register For Exam DP-203: Data Engineering on Microsoft Azure?

Exam Register Link: https://examregistration.microsoft.com/?locale=en-us&examcode=DP-203&examname=Exam%20DP-203:%20Data%20Engineering%20on%20Microsoft%20Azure&returnToLearningUrl=https%3A%2F%2Fdocs.microsoft.com%2Flearn%2Fcertifications%2Fexams%2Fdp-203

Skills measured

  • Design and implement data security (10-15%)
  • Design and develop data processing (25-30%)
  • Monitor and optimize data storage and data processing (10-15%)
  • Design and implement data storage (40-45%)

What should I know before taking the Microsoft DP-203 exam?

Microsoft offers Data Engineering on Microsoft Azure certification to those who wish to demonstrate their knowledge of data engineering on the Microsoft Cloud. The exam comprises multiple-choice questions, and each question is worth one mark. The candidates are required to attempt 60 questions in total, i.e., within 130 minutes. In order to take this exam, the candidates are required to have knowledge of the fundamental concepts related to the subject matter. Knowledge of basic cloud computing concepts (such as virtual machines, virtual networks, etc.) would be beneficial for the candidates. Microsoft DP-203 exam dumps contains an online study guide that explains all the concepts and answers to practice questions. Candidates should try to understand the concepts completely to gain good marks on the test.

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

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

  • India - 7905404 INR

  • United States - $104,000 USD

  • UK - 78707 Pounds

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

Microsoft DP-203 Korean Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Design and develop data processing25–30%- Design data processing pipelines
  • 1. Design batch processing solutions
    • 2. Design for data integration and transformation
      • 3. Design orchestration and scheduling
        • 4. Design real-time processing solutions
          - Develop data processing solutions
          • 1. Implement data ingestion
            • 2. Manage data flows and dependencies
              • 3. Transform data using Spark, SQL, or other tools
                • 4. Implement incremental processing
                  Topic 2: Design and implement data storage40–45%- Design data storage structures
                  • 1. Design for data durability and availability
                    • 2. Design partitioning strategies
                      • 3. Recommend storage solutions for analytical workloads
                        • 4. Design for scalability and performance
                          - Implement data storage
                          • 1. Manage data lifecycle and retention policies
                            • 2. Implement relational and non-relational data stores
                              • 3. Implement data lake storage
                                • 4. Create and configure data storage accounts
                                  Topic 3: Design and implement data security10–15%- Protect data at rest and in transit
                                  • 1. Manage data privacy and compliance
                                    • 2. Configure data masking and redaction
                                      • 3. Implement encryption
                                        - Implement authentication and authorization
                                        • 1. Manage access to data resources
                                          • 2. Configure identity and access management
                                            Topic 4: Monitor and optimize data storage and data processing10–15%- Monitor data solutions
                                            • 1. Monitor storage performance and health
                                              • 2. Monitor pipeline execution and errors
                                                • 3. Log and analyze activity and usage
                                                  - Optimize performance and cost
                                                  • 1. Tune pipelines and workloads
                                                    • 2. Optimize storage and compute resources
                                                      • 3. Improve query and processing efficiency

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