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All the details of Microsoft DP-203 Exam format?

  • Exam Duration: 130 minutes

  • Exam Length: 40-60 question

  • Passing score: 75%

  • Exam Format: multiple-choice

  • Language: English

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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.

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

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Microsoft DP-203日本語 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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. Implement encryption
        • 2. Configure data masking and redaction
          • 3. Manage data privacy and compliance
            Topic 2: 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. Recommend storage solutions for analytical workloads
                      • 2. Design for scalability and performance
                        • 3. Design partitioning strategies
                          • 4. Design for data durability and availability
                            Topic 3: Monitor and optimize data storage and data processing10–15%- Monitor data solutions
                            • 1. Monitor storage performance and health
                              • 2. Log and analyze activity and usage
                                • 3. Monitor pipeline execution and errors
                                  - Optimize performance and cost
                                  • 1. Optimize storage and compute resources
                                    • 2. Improve query and processing efficiency
                                      • 3. Tune pipelines and workloads
                                        Topic 4: Design and develop data processing25–30%- Design data processing pipelines
                                        • 1. Design orchestration and scheduling
                                          • 2. Design for data integration and transformation
                                            • 3. Design real-time processing solutions
                                              • 4. Design batch processing solutions
                                                - Develop data processing solutions
                                                • 1. Transform data using Spark, SQL, or other tools
                                                  • 2. Implement data ingestion
                                                    • 3. Manage data flows and dependencies
                                                      • 4. Implement incremental processing

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