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Last Updated: Aug 23, 2026

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The Microsoft DP-900 test evaluates the ability of the candidates to execute specific technical tasks. The examinees must understand these areas before they take this test. The topics that this certification exam covers are as follows:

Explain the Concepts of Core Data: 15-20%

  • Explain the Core Concepts of Data Analytics: The candidates need to be able to explain data visualization, analytics techniques, data processing concepts, ETL and ELT processing, as well as the basic types of charts, including pie charts and bar charts.
  • Explain Core Data Workloads Types: The test takers should be able to explain batch data, attributes of relational data, streaming data, and the differences between streaming and batch data.

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.

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

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