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

DP-900 Korean pass collection

Exam Code: DP-900-KR

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

Updated: Jul 28, 2026

Q & A: 320 Questions and Answers

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

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What Are the Exam Topics?

Core data concepts

This topic majorly addresses the following skills:

  • 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.
  • 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.

Microsoft DP-900 Korean Exam Syllabus Topics:

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

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