Microsoft DP-203日本語 : Data Engineering on Microsoft Azure (DP-203日本語版)

  • Exam Code: DP-203日本語
  • Exam Name: Data Engineering on Microsoft Azure (DP-203日本語版)
  • Updated: Sep 07, 2026

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About Microsoft DP-203日本語 Exam

What is Microsoft DP-203 Certification

Microsoft DP-203 (Designing Data Platform Solutions on Microsoft Azure) certification is a great way to validate your hands-on experience with cloud platforms, data architecture, and database technologies. This exam is designed to determine the competency of candidates to design distributed data solutions for enterprise applications based on Azure technologies. Before you begin studying for this exam, there are a few terms that you should familiarize yourself with. First and foremost, you should understand the difference between Microsoft SQL Server, Microsoft SQL Database, and Microsoft Azure SQL Database. Exam objectives are associated with these products; therefore, it is important to know when you will be tested on each one. Microsoft DP-203 Dumps tests a candidate's knowledge of Microsoft technologies used for building a data platform and cloud environment. Microsoft DP-203 is a certification of data engineers. The certification exam is designed to test the skills of designing, deploying, and managing Microsoft HDInsight and Microsoft Azure SQL Data Warehouse clusters for business intelligence and analytics solutions.

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

Skills measured

  • Monitor and optimize data storage and data processing (10-15%)
  • Design and develop data processing (25-30%)
  • Design and implement data security (10-15%)
  • Design and implement data storage (40-45%)

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Learn about the benefits of Microsoft DP-203 Certification

Microsoft DP-203 certification is a professional certification given to the candidates who successfully complete the DP-203 exam. Microsoft Data Platform with Hadoop Developer 203: Administration certification is an international standard for demonstrating competence in data platform administration. The exam validates the candidate's ability to administer and develop data platforms on the cloud-based environment of Microsoft Azure. The DP-203 certification is a globally recognized credential that can enable you to stand out from your peers and make your career more rewarding. The DP-203 course will help you to become a specialist who is able to manage, maintain and develop applications running on Hadoop frameworks on the Azure cloud platform. Microsoft DP-203 Dumps is designed to achieve your goal. The DP-203 training course covers the fundamental concepts of cloud computing, creating and managing virtual machines, storage accounts, load balancers, web and worker roles, databases, HDInsight, etc. It also covers how to implement security infrastructure and management of virtual networks using PowerShell commands. You will receive lifetime access to the content along with practice exam questions from real exams after each module. The DP-203 course provides an opportunity for career advancement as it enables you to enhance your expertise in developing solutions with the Hadoop framework and other data sources using the Microsoft Azure cloud platform. It will also help you boost your proficiency in implementing. Correct mapping and auditing exception testing for data.

Data Engineering on Microsoft Azure Exam Certification Details:

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

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Microsoft DP-203日本語 Exam Syllabus Topics:

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

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