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Familiarize yourself with the format of the Microsoft DP-203 Exam

Microsoft Data Platform (DP) is an exam for IT professionals who are responsible for designing and implementing data processing solutions that integrate with Microsoft platforms, applications, and services. Candidates prepare for this exam by taking the Microsoft Official Curriculum (MOC) course for the Azure Data Engineering on Microsoft Azure certification. The DP-203 exam tests your ability to design and implement data processing solutions on Azure in a cloud environment. You need to understand how to design and create data services with Azure Data Factory; how to create, manage and deploy data models using the Azure Data Catalog; and how to manage the lifecycle of a data solution using Azure Data Lake Analytics. Microsoft DP-203 Dumps Questions and Answers are prepared by experts and reviewed and approved by Microsoft. The test is based on the latest version of Microsoft Data Platform that includes SQL Server 2016, SQL Database, HDInsight, and Power BI. The questions test both technical skills and business knowledge so you need to have a good understanding of both areas in order to pass the test.

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Certification Topics of Microsoft DP-203 Exam

  • Design and implement data storage (40-45%)

  • Monitor and optimize data storage and data processing (10-15%)

  • Design and implement data security (10-15%)

  • Design and develop data processing (25-30%)

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%- 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. Optimize storage and compute resources
          • 2. Improve query and processing efficiency
            • 3. Tune pipelines and workloads
              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
                        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. Manage data flows and dependencies
                                    • 3. Implement incremental processing
                                      • 4. Implement data ingestion
                                        Design and implement data storage40–45%- Implement data storage
                                        • 1. Create and configure data storage accounts
                                          • 2. Implement data lake storage
                                            • 3. Manage data lifecycle and retention policies
                                              • 4. Implement relational and non-relational data stores
                                                - Design data storage structures
                                                • 1. Recommend storage solutions for analytical workloads
                                                  • 2. Design partitioning strategies
                                                    • 3. Design for scalability and performance
                                                      • 4. Design for data durability and availability

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