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Last Updated: Jul 30, 2026

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Data Engineering on Microsoft Azure Exam Certification Details:

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

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

Microsoft DP-203 Exam Syllabus Topics:

SectionWeightObjectives
Monitor and optimize data storage and data processing10–15%- Optimize performance and cost
  • 1. Tune pipelines and workloads
    • 2. Improve query and processing efficiency
      • 3. Optimize storage and compute resources
        - Monitor data solutions
        • 1. Monitor pipeline execution and errors
          • 2. Log and analyze activity and usage
            • 3. Monitor storage performance and health
              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. Manage data lifecycle and retention policies
                          • 3. Create and configure data storage accounts
                            • 4. Implement relational and non-relational data stores
                              Design and implement data security10–15%- Protect data at rest and in transit
                              • 1. Implement encryption
                                • 2. Manage data privacy and compliance
                                  • 3. Configure data masking and redaction
                                    - Implement authentication and authorization
                                    • 1. Manage access to data resources
                                      • 2. Configure identity and access management
                                        Design and develop data processing25–30%- Develop data processing solutions
                                        • 1. Implement data ingestion
                                          • 2. Implement incremental processing
                                            • 3. Manage data flows and dependencies
                                              • 4. Transform data using Spark, SQL, or other tools
                                                - Design data processing pipelines
                                                • 1. Design real-time processing solutions
                                                  • 2. Design batch processing solutions
                                                    • 3. Design orchestration and scheduling
                                                      • 4. Design for data integration and transformation

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