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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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What is the salary of a Microsoft DP-203 certified professional?

The Average salary of different countries of Microsoft DP-203 Certified professional

  • UK - 78707 Pounds

  • United States - $104,000 USD

  • India - 7905404 INR

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Certification Topics of Microsoft DP-203 Exam

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

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

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

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

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

All the details of Microsoft DP-203 Exam format?

  • Language: English

  • Exam Length: 40-60 question

  • Exam Format: multiple-choice

  • Exam Duration: 130 minutes

  • Passing score: 75%

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Microsoft DP-203 Deutsch Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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. Implement incremental processing
            • 2. Transform data using Spark, SQL, or other tools
              • 3. Implement data ingestion
                • 4. Manage data flows and dependencies
                  Topic 2: Design and implement data security10–15%- Protect data at rest and in transit
                  • 1. Implement encryption
                    • 2. Manage data privacy and compliance
                      • 3. Configure data masking and redaction
                        - Implement authentication and authorization
                        • 1. Manage access to data resources
                          • 2. Configure identity and access management
                            Topic 3: Design and implement data storage40–45%- Implement data storage
                            • 1. Create and configure data storage accounts
                              • 2. Manage data lifecycle and retention policies
                                • 3. Implement relational and non-relational data stores
                                  • 4. Implement data lake storage
                                    - 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
                                            Topic 4: Monitor and optimize data storage and data processing10–15%- Monitor data solutions
                                            • 1. Monitor storage performance and health
                                              • 2. Log and analyze activity and usage
                                                • 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

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