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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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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.

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Skills measured

  • Design and develop data processing (25-30%)
  • Design and implement data storage (40-45%)
  • Design and implement data security (10-15%)
  • Monitor and optimize data storage and data processing (10-15%)

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

Familiarize yourself with the format of the Microsoft DP-203 Exam

Microsoft Data Platform (DP) is an exam for IT professionals who are responsible for designing and implementing data processing solutions that integrate with Microsoft platforms, applications, and services. Candidates prepare for this exam by taking the Microsoft Official Curriculum (MOC) course for the Azure Data Engineering on Microsoft Azure certification. The DP-203 exam tests your ability to design and implement data processing solutions on Azure in a cloud environment. You need to understand how to design and create data services with Azure Data Factory; how to create, manage and deploy data models using the Azure Data Catalog; and how to manage the lifecycle of a data solution using Azure Data Lake Analytics. Microsoft DP-203 Dumps Questions and Answers are prepared by experts and reviewed and approved by Microsoft. The test is based on the latest version of Microsoft Data Platform that includes SQL Server 2016, SQL Database, HDInsight, and Power BI. The questions test both technical skills and business knowledge so you need to have a good understanding of both areas in order to pass the test.

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Microsoft DP-203 Korean Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Monitor and optimize data storage and data processing10–15%- Monitor data solutions
  • 1. Monitor storage performance and health
    • 2. Log and analyze activity and usage
      • 3. Monitor pipeline execution and errors
        - Optimize performance and cost
        • 1. Tune pipelines and workloads
          • 2. Improve query and processing efficiency
            • 3. Optimize storage and compute resources
              Topic 2: 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. Configure identity and access management
                      • 2. Manage access to data resources
                        Topic 3: Design and develop data processing25–30%- Develop data processing solutions
                        • 1. Manage data flows and dependencies
                          • 2. Implement data ingestion
                            • 3. Transform data using Spark, SQL, or other tools
                              • 4. Implement incremental processing
                                - 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
                                        Topic 4: Design and implement data storage40–45%- Implement data storage
                                        • 1. Implement data lake storage
                                          • 2. Manage data lifecycle and retention policies
                                            • 3. Implement relational and non-relational data stores
                                              • 4. Create and configure data storage accounts
                                                - Design data storage structures
                                                • 1. Design for scalability and performance
                                                  • 2. Design partitioning strategies
                                                    • 3. Design for data durability and availability
                                                      • 4. Recommend storage solutions for analytical workloads

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