Microsoft Data Engineering on Microsoft Azure - DP-203

Microsoft DP-203 test insides dumps
  • Exam Code: DP-203
  • Exam Name: Data Engineering on Microsoft Azure
  • Updated: Aug 01, 2026
  • Q & A: 365 Questions and Answers
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About Microsoft Data Engineering on Microsoft Azure : DP-203 exam dumps

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Microsoft DP-203 (Designing Data Platform Solutions on Microsoft Azure) certification is a great way to validate your hands-on experience with cloud platforms, data architecture, and database technologies. This exam is designed to determine the competency of candidates to design distributed data solutions for enterprise applications based on Azure technologies. Before you begin studying for this exam, there are a few terms that you should familiarize yourself with. First and foremost, you should understand the difference between Microsoft SQL Server, Microsoft SQL Database, and Microsoft Azure SQL Database. Exam objectives are associated with these products; therefore, it is important to know when you will be tested on each one. Microsoft DP-203 Dumps tests a candidate's knowledge of Microsoft technologies used for building a data platform and cloud environment. Microsoft DP-203 is a certification of data engineers. The certification exam is designed to test the skills of designing, deploying, and managing Microsoft HDInsight and Microsoft Azure SQL Data Warehouse clusters for business intelligence and analytics solutions.

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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

  • India - 7905404 INR

  • UK - 78707 Pounds

  • United States - $104,000 USD

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What is the cost of the Microsoft DP-203 Exam

The Microsoft DP-203 Exam cost is $165 USD.

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

Microsoft DP-203 Exam Syllabus Topics:

SectionWeightObjectives
Design and implement data storage40–45%- Design data storage structures
  • 1. Design for scalability and performance
    • 2. Design for data durability and availability
      • 3. Recommend storage solutions for analytical workloads
        • 4. Design partitioning strategies
          - Implement data storage
          • 1. Create and configure data storage accounts
            • 2. Manage data lifecycle and retention policies
              • 3. Implement data lake storage
                • 4. Implement relational and non-relational data stores
                  Design and implement data security10–15%- Protect data at rest and in transit
                  • 1. Manage data privacy and compliance
                    • 2. Configure data masking and redaction
                      • 3. Implement encryption
                        - 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 pipeline execution and errors
                                • 3. Monitor storage performance and health
                                  - Optimize performance and cost
                                  • 1. Tune pipelines and workloads
                                    • 2. Improve query and processing efficiency
                                      • 3. Optimize storage and compute resources
                                        Design and develop data processing25–30%- Develop data processing solutions
                                        • 1. Implement incremental processing
                                          • 2. Implement data ingestion
                                            • 3. Transform data using Spark, SQL, or other tools
                                              • 4. Manage data flows and dependencies
                                                - Design data processing pipelines
                                                • 1. Design real-time processing solutions
                                                  • 2. Design orchestration and scheduling
                                                    • 3. Design for data integration and transformation
                                                      • 4. Design batch processing solutions

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