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

  • Exam Code: DP-203日本語
  • Exam Name: Data Engineering on Microsoft Azure (DP-203日本語版)
  • Updated: Sep 30, 2026
  • Q & A: 365 Questions and Answers

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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

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All the details of Microsoft DP-203 Exam format?

  • Exam Length: 40-60 question

  • Language: English

  • Passing score: 75%

  • Exam Duration: 130 minutes

  • Exam Format: multiple-choice

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

Microsoft DP-203日本語 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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. Configure identity and access management
          • 2. Manage access to data resources
            Topic 2: Design and develop data processing25–30%- Design data processing pipelines
            • 1. Design batch processing solutions
              • 2. Design for data integration and transformation
                • 3. Design real-time processing solutions
                  • 4. Design orchestration and scheduling
                    - Develop data processing solutions
                    • 1. Implement incremental processing
                      • 2. Manage data flows and dependencies
                        • 3. Transform data using Spark, SQL, or other tools
                          • 4. Implement data ingestion
                            Topic 3: 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
                                        Topic 4: Design and implement data storage40–45%- Design data storage structures
                                        • 1. Design for scalability and performance
                                          • 2. Design partitioning strategies
                                            • 3. Design for data durability and availability
                                              • 4. Recommend storage solutions for analytical workloads
                                                - Implement data storage
                                                • 1. Create and configure data storage accounts
                                                  • 2. Implement relational and non-relational data stores
                                                    • 3. Implement data lake storage
                                                      • 4. Manage data lifecycle and retention policies

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