Microsoft DP-900 Korean : Microsoft Azure Data Fundamentals (DP-900 Korean Version)

  • Exam Code: DP-900 Korean
  • Exam Name: Microsoft Azure Data Fundamentals (DP-900 Korean Version)
  • Updated: Jul 27, 2026     Q & A: 320 Questions and Answers

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Exam Details

The Microsoft DP-900 exam has the following features:

  • It costs $99. As for the job seekers impacted by the pandemic (COVID -19), they can schedule their exam for just $15.
  • The number of questions is not disclosed but you should expect 40-60 items
  • The registration for the test is done at Pearson VUE for non-students who are interested in technology and via Certiport for students or instructors.

Upon passing DP-900, you will earn the Microsoft Certified: Azure Data Fundamentals. It showcases that you have the foundational knowledge on core data concepts and how they can be implemented using Microsoft Azure. Note that Microsoft DP-900 will be updated on January 27, 2021.

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Explain Analytics Workloads on Azure: 25-30%

  • Explain Modern Data Warehouse Components: The test takers should understand Azure data services for the latest data warehousing, including Azure Synapse Analytics, Azure HDInsight, Azure Data Lake, and Azure Databricks, as well as explainin the workload and architecture for modern data warehousing.
  • Explain Data Processing & Ingestion on Azure: The learners should be able to explain the fundamental practices for data loading, Azure Data Factory components, and data processing options.
  • Explain Data Visualization within Microsoft Power BI: You need to have competence in explaining the function of dashboards, interactive reports, and paginated reporting, as well as the workflow within Power BI.
  • Explain Analytics Workloads: The applicants should be able to explain transactional workloads, data warehousing workloads, and when data warehouse solutions are needed. This subtopic also covers their skills and knowledge associated with the differences between analytics and a transactional workload as well as the differences between real-time and batch.

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/dp-900

Career Prospects

  • Systems Engineer – this role will keep you engaged in the design and the development of the cloud solutions that are appropriate to the business at hand. These professionals also analyze both the system and software requirements before giving design guidelines. This role is estimated to earn you an average salary of $81k per annum, as per PayScale.
  • Cloud Solutions Analyst – an early career as cloud solutions analyst or architect is quite lucrative. You can earn up to an average salary of $80k annually with even less than 1 year of working experience in this role. Once you develop in this job, the salary may rise up to the average of $126k per annum. Although the role is so much demanding, you should be ready to do proper evaluation and performance testing of a cloud solution before integration. The main task of these specialists is to ensure that the performance of the system is optimized.
  • Systems Administrator – as a sysadmin, you will be tasked with providing hands-on support when it comes to system maintenance, operation and administration. You will also be the person concerned in making recommendation of an appropriate software to be deployed. The average salary is estimated to be $63k.

Microsoft DP-900 Korean Exam Syllabus Topics:

SectionWeightObjectives
Describe considerations for working with non-relational data on Azure15–20%- Describe Azure Cosmos DB
  • 1. APIs and data models
    • 2. Consistency levels and partitioning
      • 3. Capabilities and features
        - Describe non-relational data concepts
        • 1. Characteristics of NoSQL data
          • 2. Use cases for non-relational data
            - Describe Azure storage services
            • 1. File Storage
              • 2. Table Storage
                • 3. Queue Storage
                  • 4. Blob Storage
                    Identify considerations for relational data on Azure20–25%- Describe relational data concepts
                    • 1. Relationships, keys, constraints
                      • 2. Query data with SQL
                        • 3. Normalization
                          - Describe Azure relational data services
                          • 1. SQL Server on Azure Virtual Machines
                            • 2. Azure SQL Managed Instance
                              • 3. Open-source databases: PostgreSQL, MySQL, MariaDB
                                • 4. Azure SQL Database
                                  Describe an analytics workload on Azure25–30%- Describe big data and analytics concepts
                                  • 1. Data lakes and data warehouses
                                    • 2. Data ingestion and processing
                                      • 3. Modern data warehouse architecture
                                        - Describe data visualization
                                        • 1. Data models and reports
                                          • 2. Power BI capabilities
                                            - Describe real-time analytics
                                            • 1. Stream processing concepts
                                              • 2. Azure Stream Analytics
                                                - Describe Azure analytics services
                                                • 1. Microsoft Fabric
                                                  • 2. Azure Databricks
                                                    • 3. Azure Data Lake Storage Gen2
                                                      • 4. Azure HDInsight
                                                        • 5. Azure Synapse Analytics
                                                          Describe core data concepts25–30%- Describe types of data
                                                          • 1. File formats
                                                            • 2. Structured, semi-structured, and unstructured data
                                                              - Describe data workloads
                                                              • 1. Batch and streaming data
                                                                • 2. Transactional workloads
                                                                  • 3. Analytical workloads
                                                                    - Describe roles and responsibilities
                                                                    • 1. Data engineer, data analyst, database administrator

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