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

Microsoft DP-900 Korean test insides dumps
  • Exam Code: DP-900-KR
  • Exam Name: Microsoft Azure Data Fundamentals (DP-900 Korean Version)
  • Updated: Oct 01, 2026
  • Q & A: 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.

The Microsoft DP-900 (Microsoft Azure Data Fundamentals) is the single exam students will have to overcome if they want to attain the Microsoft Certified: Azure Data Fundamentals certificate. Those who hold this certification can become a developer, data engineer, data administrator, or data analyst.

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

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How to book the Microsoft DP-900: Microsoft Azure Data Fundamentals Exam

  • Step 1: Visit Microsoft Learning and search for DP-900: Microsoft Azure fundamentals.
  • Step 2: Sign up/log in to the Pearson VUE account
  • Step 3: Select a local center based on your country, date, time and confirm with a payment method.

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Helpful Training Course for Acing DP-900

To help students get fully ready for DP-900, Microsoft has created an official 1-day teacher-guided program for students named ‘Course DP-900T00-A: Microsoft Azure Data Fundamentals’. There are 4 modules in this course which will address all the topics needed for the Microsoft DP-900. However, such a class does not have any related lab exercises, meaning students will not get the scope for applying the things they have learnt in theory. In general, this training is perfect for those who want to learn the main concepts and skills needed to provide cloud data services with the help of Microsoft Azure.

Microsoft DP-900 Korean Exam Syllabus Topics:
SectionWeightObjectives
Describe an analytics workload on Azure25–30%- Describe Azure analytics services
  • 1. Azure Databricks
    • 2. Azure HDInsight
      • 3. Azure Data Lake Storage Gen2
        • 4. Azure Synapse Analytics
          • 5. Microsoft Fabric
            - Describe data visualization
            • 1. Power BI capabilities
              • 2. Data models and reports
                - Describe real-time analytics
                • 1. Stream processing concepts
                  • 2. Azure Stream Analytics
                    - Describe big data and analytics concepts
                    • 1. Modern data warehouse architecture
                      • 2. Data lakes and data warehouses
                        • 3. Data ingestion and processing
                          Identify considerations for relational data on Azure20–25%- Describe relational data concepts
                          • 1. Query data with SQL
                            • 2. Normalization
                              • 3. Relationships, keys, constraints
                                - 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 non-relational data concepts
                                        • 1. Characteristics of NoSQL data
                                          • 2. Use cases for non-relational data
                                            - Describe Azure storage services
                                            • 1. Table Storage
                                              • 2. Queue Storage
                                                • 3. File Storage
                                                  • 4. Blob Storage
                                                    - Describe Azure Cosmos DB
                                                    • 1. Capabilities and features
                                                      • 2. APIs and data models
                                                        • 3. Consistency levels and partitioning
                                                          Describe core data concepts25–30%- Describe data workloads
                                                          • 1. Analytical workloads
                                                            • 2. Batch and streaming data
                                                              • 3. Transactional workloads
                                                                - Describe roles and responsibilities
                                                                • 1. Data engineer, data analyst, database administrator
                                                                  - Describe types of data
                                                                  • 1. Structured, semi-structured, and unstructured data
                                                                    • 2. File formats

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