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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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Data Engineering on Microsoft Azure Exam Certification Details:

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

How someone with a Microsoft DP-203 certificate will be better off?

There is no doubt that the DP-203 certificate on Microsoft Azure will be helpful in showing future employers and clients that you have a good understanding of the Microsoft Azure platform and have a sound knowledge of data management, data processing, and business intelligence. You can use this DP-203 certification to demonstrate your ability to build an enterprise-class data warehousing solution using Microsoft Azure's fully managed services. Microsoft DP-203 Dumps is the best way to ensure that you pass the exam on the first attempt. With these Microsoft DP-203 Practice Tests, you will be able to test your preparation before the real exam. After completing this course, you will be able to: Describe the challenges for data warehousing in the cloud. Understand how cloud storage works with Azure SQL Data Warehouse. Implement a relational database in the cloud using Azure SQL Database Managed Instance. Deploy a highly available and scalable data warehouse using Azure SQL Data Warehouse. External workloads load efficient nodes repartitioning folder selection guides duplicate hierarchy. Loading, archiving, pruning, premises, tabular, defined dimensional purposes. Stream table pipelines distribution handling control region temporal incremental dimensions structure tool. Demo PDF is also available.

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

Microsoft DP-203 Deutsch Exam Syllabus Topics:

SectionWeightObjectives
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
            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. Tune pipelines and workloads
                    • 2. Improve query and processing efficiency
                      • 3. Optimize storage and compute resources
                        Design and develop data processing25–30%- Develop data processing solutions
                        • 1. Implement incremental processing
                          • 2. Manage data flows and dependencies
                            • 3. Implement data ingestion
                              • 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 batch processing solutions
                                      • 4. Design for data integration and transformation
                                        Design and implement data storage40–45%- Implement data storage
                                        • 1. Implement data lake storage
                                          • 2. Manage data lifecycle and retention policies
                                            • 3. Create and configure data storage accounts
                                              • 4. Implement relational and non-relational data stores
                                                - Design data storage structures
                                                • 1. Design for data durability and availability
                                                  • 2. Design for scalability and performance
                                                    • 3. Design partitioning strategies
                                                      • 4. Recommend storage solutions for analytical workloads

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