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Microsoft DP-203日本語

DP-203日本語

Exam Code: DP-203J

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

Updated: Oct 16, 2026

Q&A Number: 365 Q&As

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About Microsoft DP-203日本語 Exam Questions and Answers

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Skills measured

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

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

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Exam DP-203: Data Engineering on Microsoft Azure

Candidates for this exam should have subject matter expertise integrating, transforming, and consolidating data from various structured and unstructured data systems into a structure that is suitable for building analytics solutions.

Azure Data Engineers help stakeholders understand the data through exploration, and they build and maintain secure and compliant data processing pipelines by using different tools and techniques. These professionals use various Azure data services and languages to store and produce cleansed and enhanced datasets for analysis.

Azure Data Engineers also help ensure that data pipelines and data stores are high-performing, efficient, organized, and reliable, given a set of business requirements and constraints. They deal with unanticipated issues swiftly, and they minimize data loss. They also design, implement, monitor, and optimize data platforms to meet the data pipelines needs.

A candidate for this exam must have strong knowledge of data processing languages such as SQL, Python, or Scala, and they need to understand parallel processing and data architecture patterns.

Part of the requirements for: Microsoft Certified: Azure Data Engineer Associate

Download exam skills outline

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

Microsoft DP-203 Exam Reference link

Microsoft DP-203日本語 Exam Syllabus Topics:

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

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