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

  • Exam Code: DP-900-KR
  • Exam Name: Microsoft Azure Data Fundamentals (DP-900 Korean Version)
  • Updated: Aug 31, 2026
  • Q&As: 332 Questions and Answers

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Prove that you can describe the following: core data concepts; how to work with relational data on Azure; how to work with non-relational data on Azure; and an analytics workload on Azure.

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

What Are the Exam Topics?

Core data concepts

This topic majorly addresses the following skills:

  • Various data workloads – this involves an overview of the features of relational data as well as the description, similarities and differences between streaming data and batch data as well as.
  • Main concepts of data analytics – data visualization, business intelligence (BI), analytics techniques (diagnostic, predictive, descriptive, cognitive, prescriptive) and basic chart types such as pie charts and bar charts are the main concepts addressed in this category. Also, data processing techniques are covered along with ETL processing.

Microsoft DP-900 Korean Exam Syllabus Topics:

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

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