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Data Engineering on Microsoft Azure Exam Certification Details:

Sample QuestionsData Engineering on Microsoft Azure Sample Questions
Duration150 mins
Number of Questions40-60
Exam Price$165 (USD)
Schedule ExamPearson VUE
Exam NameMicrosoft Certified - Azure Data Engineer Associate
Passing Score700 / 1000
Exam CodeDP-203
Books / TrainingDP-203T00: Data Engineering on Microsoft Azure

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

Microsoft DP-203日本語 Exam Syllabus Topics:

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

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