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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

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

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Microsoft DP-203 Korean Exam Syllabus Topics:

SectionWeightObjectives
Design and develop data processing25–30%- Design data processing pipelines
  • 1. Design orchestration and scheduling
    • 2. Design for data integration and transformation
      • 3. Design batch processing solutions
        • 4. Design real-time processing solutions
          - Develop data processing solutions
          • 1. Transform data using Spark, SQL, or other tools
            • 2. Implement incremental processing
              • 3. Implement data ingestion
                • 4. Manage data flows and dependencies
                  Monitor and optimize data storage and data processing10–15%- Optimize performance and cost
                  • 1. Tune pipelines and workloads
                    • 2. Optimize storage and compute resources
                      • 3. Improve query and processing efficiency
                        - Monitor data solutions
                        • 1. Monitor pipeline execution and errors
                          • 2. Monitor storage performance and health
                            • 3. Log and analyze activity and usage
                              Design and implement data storage40–45%- Design data storage structures
                              • 1. Design for data durability and availability
                                • 2. Design partitioning strategies
                                  • 3. Design for scalability and performance
                                    • 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
                                              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. Configure data masking and redaction
                                                    • 2. Manage data privacy and compliance
                                                      • 3. Implement encryption

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