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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

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How to schedule for Microsoft DP-203 Exam

The DP-203 exam is offered through Pearson VUE test centers at various locations across the country. To register for the DP-203 exam, follow these steps: Go to Microsoft DP-203 Exam.

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

What is the cost of the Microsoft DP-203 Exam

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

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Microsoft DP-203日本語 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Design and implement data storage40–45%- Implement data storage
  • 1. Create and configure data storage accounts
    • 2. Implement data lake storage
      • 3. Manage data lifecycle and retention policies
        • 4. Implement relational and non-relational data stores
          - Design data storage structures
          • 1. Design for scalability and performance
            • 2. Recommend storage solutions for analytical workloads
              • 3. Design partitioning strategies
                • 4. Design for data durability and availability
                  Topic 2: Design and develop data processing25–30%- 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
                          - Design data processing pipelines
                          • 1. Design for data integration and transformation
                            • 2. Design orchestration and scheduling
                              • 3. Design real-time processing solutions
                                • 4. Design batch processing solutions
                                  Topic 3: Monitor and optimize data storage and data processing10–15%- Optimize performance and cost
                                  • 1. Optimize storage and compute resources
                                    • 2. Tune pipelines and workloads
                                      • 3. Improve query and processing efficiency
                                        - Monitor data solutions
                                        • 1. Log and analyze activity and usage
                                          • 2. Monitor storage performance and health
                                            • 3. Monitor pipeline execution and errors
                                              Topic 4: Design and implement data security10–15%- Implement authentication and authorization
                                              • 1. Manage access to data resources
                                                • 2. Configure identity and access management
                                                  - 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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