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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.

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

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Microsoft DP-203 Deutsch Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Design and implement data security10–15%- Protect data at rest and in transit
  • 1. Manage data privacy and compliance
    • 2. Configure data masking and redaction
      • 3. Implement encryption
        - Implement authentication and authorization
        • 1. Manage access to data resources
          • 2. Configure identity and access management
            Topic 2: Monitor and optimize data storage and data processing10–15%- Monitor data solutions
            • 1. Monitor pipeline execution and errors
              • 2. Log and analyze activity and usage
                • 3. Monitor storage performance and health
                  - Optimize performance and cost
                  • 1. Improve query and processing efficiency
                    • 2. Optimize storage and compute resources
                      • 3. Tune pipelines and workloads
                        Topic 3: Design and develop data processing25–30%- Develop data processing solutions
                        • 1. Transform data using Spark, SQL, or other tools
                          • 2. Manage data flows and dependencies
                            • 3. Implement incremental processing
                              • 4. Implement data ingestion
                                - Design data processing pipelines
                                • 1. Design real-time processing solutions
                                  • 2. Design batch processing solutions
                                    • 3. Design for data integration and transformation
                                      • 4. Design orchestration and scheduling
                                        Topic 4: Design and implement data storage40–45%- Implement data storage
                                        • 1. Implement relational and non-relational data stores
                                          • 2. Implement data lake storage
                                            • 3. Manage data lifecycle and retention policies
                                              • 4. Create and configure data storage accounts
                                                - Design data storage structures
                                                • 1. Design for data durability and availability
                                                  • 2. Recommend storage solutions for analytical workloads
                                                    • 3. Design partitioning strategies
                                                      • 4. Design for scalability and performance

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