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Data Engineering on Microsoft Azure Exam Certification Details:

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

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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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What is the cost of the Microsoft DP-203 Exam

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

How to Register For Exam DP-203: Data Engineering on Microsoft Azure?

Exam Register Link: https://examregistration.microsoft.com/?locale=en-us&examcode=DP-203&examname=Exam%20DP-203:%20Data%20Engineering%20on%20Microsoft%20Azure&returnToLearningUrl=https%3A%2F%2Fdocs.microsoft.com%2Flearn%2Fcertifications%2Fexams%2Fdp-203

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

Microsoft DP-203 Deutsch Exam Syllabus Topics:

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

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