Microsoft Data Engineering on Microsoft Azure (DP-203日本語版) : DP-203日本語

  • Exam Code: DP-203J
  • Exam Name: Data Engineering on Microsoft Azure (DP-203日本語版)
  • Updated: Aug 24, 2026     Q & A: 365 Questions and Answers

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For more information about the Microsoft DP-203 Exam visit the following reference link:

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What are the best resources to use when studying for the exam, and what is the most cost-effective way to prepare?

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

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

  • India - 7905404 INR

  • UK - 78707 Pounds

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Certification Topics of Microsoft DP-203 Exam

  • Monitor and optimize data storage and data processing (10-15%)

  • Design and implement data security (10-15%)

  • Design and develop data processing (25-30%)

  • Design and implement data storage (40-45%)

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Microsoft DP-203日本語 Exam Syllabus Topics:

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

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