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Data engineering is a crucial field that involves the development, testing, and maintenance of architectures, algorithms, and systems for capturing, storing, processing, and analyzing data. The Microsoft DP-203 certification exam is designed to test the candidate's knowledge of data engineering concepts and tools, including Azure Data Factory, Azure Synapse Analytics, Azure Data Lake Storage, and Azure Stream Analytics. DP-203 exam is essential for data engineers who want to advance their careers in the field and stay up-to-date with the latest Azure technologies.

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

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Microsoft DP-203 (Data Engineering on Microsoft Azure) Certification Exam is one of the most popular certification exams in the data engineering domain. It is designed for professionals who want to validate their skills in working with data engineering technologies on the Microsoft Azure platform. Data Engineering on Microsoft Azure certification exam is intended to showcase the proficiency of candidates in designing, building, and maintaining data processing systems on Azure.

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Microsoft DP-203 Exam Syllabus Topics:

SectionWeightObjectives
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
            Monitor and optimize data storage and data processing10–15%- Monitor data solutions
            • 1. Log and analyze activity and usage
              • 2. Monitor storage performance and health
                • 3. Monitor pipeline execution and errors
                  - Optimize performance and cost
                  • 1. Optimize storage and compute resources
                    • 2. Tune pipelines and workloads
                      • 3. Improve query and processing efficiency
                        Design and develop data processing25–30%- 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
                                - Develop data processing solutions
                                • 1. Implement data ingestion
                                  • 2. Manage data flows and dependencies
                                    • 3. Implement incremental processing
                                      • 4. Transform data using Spark, SQL, or other tools
                                        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 for scalability and performance
                                              • 4. Design partitioning strategies
                                                - Implement data storage
                                                • 1. Implement data lake storage
                                                  • 2. Implement relational and non-relational data stores
                                                    • 3. Create and configure data storage accounts
                                                      • 4. Manage data lifecycle and retention policies

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