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Last Updated: Aug 05, 2026

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Data Engineering on Microsoft Azure Exam Certification Details:

Schedule ExamPearson VUE
Passing Score700 / 1000
Sample QuestionsData Engineering on Microsoft Azure Sample Questions
Books / TrainingDP-203T00: Data Engineering on Microsoft Azure
Exam NameMicrosoft Certified - Azure Data Engineer Associate
Exam Price$165 (USD)
Number of Questions40-60
Exam CodeDP-203
Duration150 mins

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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
Topic 1: Design and implement data security10–15%- Protect data at rest and in transit
  • 1. Configure data masking and redaction
    • 2. Implement encryption
      • 3. Manage data privacy and compliance
        - Implement authentication and authorization
        • 1. Manage access to data resources
          • 2. Configure identity and access management
            Topic 2: 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 pipeline execution and errors
                      • 3. Monitor storage performance and health
                        Topic 3: Design and implement data storage40–45%- Design data storage structures
                        • 1. Design for data durability and availability
                          • 2. Recommend storage solutions for analytical workloads
                            • 3. Design partitioning strategies
                              • 4. Design for scalability and performance
                                - Implement data storage
                                • 1. Manage data lifecycle and retention policies
                                  • 2. Implement data lake storage
                                    • 3. Create and configure data storage accounts
                                      • 4. Implement relational and non-relational data stores
                                        Topic 4: Design and develop data processing25–30%- Develop data processing solutions
                                        • 1. Implement incremental processing
                                          • 2. Transform data using Spark, SQL, or other tools
                                            • 3. Implement data ingestion
                                              • 4. Manage data flows and dependencies
                                                - 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

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