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Why do you need to get Microsoft DP-203 Exam

Are you ready to take the Data Engineering on Microsoft Azure Exam

DP-203 Exam: Key Points and Focus Areas

The Microsoft Data Platform, or MDP, is a collection of products and services that primarily target companies whose business involves large volumes of data. These companies are often referred to as data-driven businesses. The MDP helps such businesses store, process, and analyze the massive amounts of data that their operations produce. The Microsoft Data Platform Certification (Microsoft DP-203) exam tests a candidate's knowledge of the Microsoft technologies that are used to build a cloud computing infrastructure for Big Data analytics. Free demo material that helps with the preparation for the DP-203 exam includes sample questions and answers, practice exams, and a training guide. Microsoft DP-203 Dumps provide all the test-taking tools necessary for passing the DP-203 exam, including a test engine and a simulation mode. If you are reading this article it means you are interested in pursuing a Microsoft certification path or already have one. Great! You've taken an important first step to become a Data Engineer on Microsoft Azure certification.

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

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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

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Microsoft DP-203 Korean Exam Syllabus Topics:

SectionWeightObjectives
Design and implement data storage40–45%- Implement data storage
  • 1. Create and configure data storage accounts
    • 2. Implement data lake storage
      • 3. Implement relational and non-relational data stores
        • 4. Manage data lifecycle and retention policies
          - Design data storage structures
          • 1. Recommend storage solutions for analytical workloads
            • 2. Design for scalability and performance
              • 3. Design for data durability and availability
                • 4. Design partitioning strategies
                  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. Optimize storage and compute resources
                          • 2. Tune pipelines and workloads
                            • 3. Improve query and processing efficiency
                              Design and implement data security10–15%- Protect data at rest and in transit
                              • 1. Configure data masking and redaction
                                • 2. Manage data privacy and compliance
                                  • 3. Implement encryption
                                    - Implement authentication and authorization
                                    • 1. Manage access to data resources
                                      • 2. Configure identity and access management
                                        Design and develop data processing25–30%- Develop data processing solutions
                                        • 1. Manage data flows and dependencies
                                          • 2. Transform data using Spark, SQL, or other tools
                                            • 3. Implement data ingestion
                                              • 4. Implement incremental processing
                                                - Design data processing pipelines
                                                • 1. Design real-time processing solutions
                                                  • 2. Design batch processing solutions
                                                    • 3. Design orchestration and scheduling
                                                      • 4. Design for data integration and transformation

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