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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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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

Microsoft DP-203日本語 Exam Syllabus Topics:

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

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