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  • Exam Code: DP-203-Deutsch
  • Exam Name: Data Engineering on Microsoft Azure (DP-203 Deutsch Version)
  • Last Updated: Sep 01, 2026
  • Q & A: 365 Questions and Answers
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  • Exam Code: DP-203-Deutsch
  • Exam Name: Data Engineering on Microsoft Azure (DP-203 Deutsch Version)
  • Last Updated: Sep 01, 2026
  • Q & A: 365 Questions and Answers
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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.

Data Engineering on Microsoft Azure Exam Certification Details:

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

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

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Microsoft DP-203 Deutsch Exam Syllabus Topics:

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

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