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Microsoft DP-203 exam is suitable for those who have experience with Microsoft Azure and want to specialize in data engineering. DP-203 exam covers a wide range of topics such as data processing, data storage, data transformation, and data integration. It tests the candidate's ability to design, implement, and monitor data processing solutions on Azure.

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To prepare for the DP-203 certification exam, candidates must have a solid understanding of Azure data technologies, including Azure Data Factory, Azure Databricks, Azure Stream Analytics, and Azure Synapse Analytics. Candidates must also have experience in designing, building, and maintaining data processing systems. Microsoft offers a variety of resources to help candidates prepare for the DP-203 certification exam, including online training courses, practice tests, and study guides.

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Microsoft DP-203 (Data Engineering on Microsoft Azure) Exam is designed to test the skills and knowledge of professionals who work with data engineering on the Azure platform. DP-203 exam is part of the Microsoft Certified: Azure Data Engineer Associate certification and is intended for individuals who have experience building and maintaining data pipelines, implementing data storage solutions, and performing data processing using Azure services.

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

Microsoft DP-203 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Design and develop data processing25–30%- Develop data processing solutions
  • 1. Manage data flows and dependencies
    • 2. Implement incremental processing
      • 3. Transform data using Spark, SQL, or other tools
        • 4. Implement data ingestion
          - Design data processing pipelines
          • 1. Design batch processing solutions
            • 2. Design orchestration and scheduling
              • 3. Design for data integration and transformation
                • 4. Design real-time processing solutions
                  Topic 2: 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
                                  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. Improve query and processing efficiency
                                            • 3. Optimize storage and compute resources
                                              Topic 4: Design and implement data security10–15%- Implement authentication and authorization
                                              • 1. Manage access to data resources
                                                • 2. Configure identity and access management
                                                  - Protect data at rest and in transit
                                                  • 1. Implement encryption
                                                    • 2. Configure data masking and redaction
                                                      • 3. Manage data privacy and compliance
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