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

What should I know before taking the Microsoft DP-203 exam?

Microsoft offers Data Engineering on Microsoft Azure certification to those who wish to demonstrate their knowledge of data engineering on the Microsoft Cloud. The exam comprises multiple-choice questions, and each question is worth one mark. The candidates are required to attempt 60 questions in total, i.e., within 130 minutes. In order to take this exam, the candidates are required to have knowledge of the fundamental concepts related to the subject matter. Knowledge of basic cloud computing concepts (such as virtual machines, virtual networks, etc.) would be beneficial for the candidates. Microsoft DP-203 exam dumps contains an online study guide that explains all the concepts and answers to practice questions. Candidates should try to understand the concepts completely to gain good marks on the test.

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Microsoft DP-203 Exam Reference link

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Microsoft DP-203 Deutsch Exam Syllabus Topics:

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

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