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  • Exam Code: DP-203日本語
  • Exam Name: Data Engineering on Microsoft Azure (DP-203日本語版)
  • Updated: Aug 01, 2026
  • Q & A: 365 Questions and Answers
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  • Exam Code: DP-203日本語
  • Exam Name: Data Engineering on Microsoft Azure (DP-203日本語版)
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  • Exam Code: DP-203日本語
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Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/dp-203

Microsoft DP-203日本語 Exam Syllabus Topics:

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

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