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

DP-203日本語 actual test
  • Exam Code: DP-203J
  • Exam Name: Data Engineering on Microsoft Azure (DP-203日本語版)
  • Updated: Jul 30, 2026
  • Q & A: 365 Questions and Answers
  • PDF Demo
  • PC Test Engine
  • Online Test Engine
  • Total Price: $69.99  

About Microsoft DP-203日本語 Exam

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Languages: English, Chinese (Simplified), Japanese, Korean

Retirement date: none

This exam measures your ability to accomplish the following technical tasks: design and implement data storage; design and develop data processing; design and implement data security; and monitor and optimize data storage and data processing.

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

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

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Microsoft DP-203日本語 Exam Syllabus Topics:

SectionWeightObjectives
Design and develop data processing25–30%- 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 data processing pipelines
          • 1. Design batch processing solutions
            • 2. Design orchestration and scheduling
              • 3. Design real-time processing solutions
                • 4. Design for data integration and transformation
                  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. Manage data privacy and compliance
                        • 2. Configure data masking and redaction
                          • 3. Implement encryption
                            Design and implement data storage40–45%- Design data storage structures
                            • 1. Design for data durability and availability
                              • 2. Design for scalability and performance
                                • 3. Recommend storage solutions for analytical workloads
                                  • 4. Design partitioning strategies
                                    - 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
                                            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 pipeline execution and errors
                                                    • 2. Log and analyze activity and usage
                                                      • 3. Monitor storage performance and health

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