Exam Code: DP-203
Exam Name: Data Engineering on Microsoft Azure (DP-203日本語版)
Certification Provider: Microsoft
Corresponding Certification: Microsoft Certified: Azure Data Engineer Associate
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Skills measured

  • 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

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Exam DP-203: Data Engineering on Microsoft Azure

Candidates for this exam should have subject matter expertise integrating, transforming, and consolidating data from various structured and unstructured data systems into a structure that is suitable for building analytics solutions.

Azure Data Engineers help stakeholders understand the data through exploration, and they build and maintain secure and compliant data processing pipelines by using different tools and techniques. These professionals use various Azure data services and languages to store and produce cleansed and enhanced datasets for analysis.

Azure Data Engineers also help ensure that data pipelines and data stores are high-performing, efficient, organized, and reliable, given a set of business requirements and constraints. They deal with unanticipated issues swiftly, and they minimize data loss. They also design, implement, monitor, and optimize data platforms to meet the data pipelines needs.

A candidate for this exam must have strong knowledge of data processing languages such as SQL, Python, or Scala, and they need to understand parallel processing and data architecture patterns.

Part of the requirements for: Microsoft Certified: Azure Data Engineer Associate

Download exam skills outline

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What is Microsoft DP-203 Certification

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.

Microsoft DP-203日本語 Exam Syllabus Topics:

SectionWeightObjectives
Design and implement data security10–15%- Protect data at rest and in transit
  • 1. Configure data masking and redaction
    • 2. Implement encryption
      • 3. Manage data privacy and compliance
        - Implement authentication and authorization
        • 1. Configure identity and access management
          • 2. Manage access to data resources
            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 data ingestion
                      • 2. Manage data flows and dependencies
                        • 3. Implement incremental processing
                          • 4. Transform data using Spark, SQL, or other tools
                            Design and implement data storage40–45%- Design data storage structures
                            • 1. Design partitioning strategies
                              • 2. Design for scalability and performance
                                • 3. Design for data durability and availability
                                  • 4. Recommend storage solutions for analytical workloads
                                    - Implement data storage
                                    • 1. Create and configure data storage accounts
                                      • 2. Implement data lake storage
                                        • 3. Implement relational and non-relational data stores
                                          • 4. Manage data lifecycle and retention policies
                                            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. Tune pipelines and workloads
                                                    • 2. Improve query and processing efficiency
                                                      • 3. Optimize storage and compute resources

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