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

DP-900 Korean real exams

Exam Code: DP-900-KR

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

Updated: Aug 09, 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 registration for the test is done at Pearson VUE for non-students who are interested in technology and via Certiport for students or instructors.
  • The number of questions is not disclosed but you should expect 40-60 items

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.

Certification Paths Available

Upon earning Microsoft Certified: Azure Data Fundamentals, you will be able to advance your career to higher-level certifications. For instance, it can be a starting point for Microsoft Certified: Azure Data Engineer Associate or Microsoft Certified: Azure Database Administrator Associate. However, this fundamental accreditation is not a requirement for earning any of the next certificates. Therefore, all you have to do is to pass Microsoft DP-900 exam and prepare to face the real-world challenges as you advance in your career.

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

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Microsoft DP-900 Korean Exam Syllabus Topics:

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