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How to schedule for Microsoft DP-203 Exam

The DP-203 exam is offered through Pearson VUE test centers at various locations across the country. To register for the DP-203 exam, follow these steps: Go to Microsoft DP-203 Exam.

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

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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.

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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Microsoft DP-203 Deutsch Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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. Optimize storage and compute resources
            • 3. Tune pipelines and workloads
              Topic 2: Design and develop data processing25–30%- Develop data processing solutions
              • 1. Implement incremental processing
                • 2. Implement data ingestion
                  • 3. Manage data flows and dependencies
                    • 4. Transform data using Spark, SQL, or other tools
                      - 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 3: Design and implement data storage40–45%- Implement data storage
                              • 1. Manage data lifecycle and retention policies
                                • 2. Implement data lake storage
                                  • 3. Implement relational and non-relational data stores
                                    • 4. Create and configure data storage accounts
                                      - Design data storage structures
                                      • 1. Recommend storage solutions for analytical workloads
                                        • 2. Design for scalability and performance
                                          • 3. Design partitioning strategies
                                            • 4. Design for data durability and availability
                                              Topic 4: 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

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