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How to Register For Exam DP-203: Data Engineering on Microsoft Azure?

Exam Register Link: https://examregistration.microsoft.com/?locale=en-us&examcode=DP-203&examname=Exam%20DP-203:%20Data%20Engineering%20on%20Microsoft%20Azure&returnToLearningUrl=https%3A%2F%2Fdocs.microsoft.com%2Flearn%2Fcertifications%2Fexams%2Fdp-203

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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
Topic 1: Design and implement data storage40–45%- Design data storage structures
  • 1. Recommend storage solutions for analytical workloads
    • 2. Design for scalability and performance
      • 3. Design for data durability and availability
        • 4. Design partitioning strategies
          - Implement data storage
          • 1. Create and configure data storage accounts
            • 2. Implement data lake storage
              • 3. Implement relational and non-relational data stores
                • 4. Manage data lifecycle and retention policies
                  Topic 2: 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. Implement encryption
                        • 2. Manage data privacy and compliance
                          • 3. Configure data masking and redaction
                            Topic 3: Monitor and optimize data storage and data processing10–15%- Monitor data solutions
                            • 1. Monitor pipeline execution and errors
                              • 2. Log and analyze activity and usage
                                • 3. Monitor storage performance and health
                                  - Optimize performance and cost
                                  • 1. Improve query and processing efficiency
                                    • 2. Tune pipelines and workloads
                                      • 3. Optimize storage and compute resources
                                        Topic 4: Design and develop data processing25–30%- Design data processing pipelines
                                        • 1. Design orchestration and scheduling
                                          • 2. Design batch processing solutions
                                            • 3. Design real-time processing solutions
                                              • 4. Design for data integration and transformation
                                                - Develop data processing solutions
                                                • 1. Transform data using Spark, SQL, or other tools
                                                  • 2. Implement incremental processing
                                                    • 3. Manage data flows and dependencies
                                                      • 4. Implement data ingestion

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