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

DP-900 Deutsch actual test
  • Exam Code: DP-900-Deutsch
  • Exam Name: Microsoft Azure Data Fundamentals (DP-900 Deutsch Version)
  • Updated: Aug 09, 2026
  • Q & A: 320 Questions and Answers
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  • Total Price: $69.99  

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The DP-900: Microsoft Azure Data Fundamentals exam is designed for the individuals pursuing the Microsoft Certified: Azure Data Fundamentals certification. This test evaluates their foundational knowledge of core concepts of data and how these are implemented with the use of Microsoft Azure data services. The associated certificate is an ideal choice for those professionals who want to start a career in the IT field with the main focus on working with data in Cloud. The successful candidates with this certification can continue their journey by earning more advanced options such as Microsoft Certified: Azure Data Engineer Associate or Microsoft Certified: Azure Database Administrator Associate. However, it is important to mention that this exam is not a requirement for any of these certificates.

This is a foundational certification exam, so it does not require any prerequisites. However, the potential candidates should have a good knowledge of the concepts of non-relational and relational data as well as different categories of data workloads, including analytical and transactional.

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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 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.
  • 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.

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

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Explain Analytics Workloads on Azure: 25-30%

  • Explain Modern Data Warehouse Components: The test takers should understand Azure data services for the latest data warehousing, including Azure Synapse Analytics, Azure HDInsight, Azure Data Lake, and Azure Databricks, as well as explainin the workload and architecture for modern data warehousing.
  • Explain Data Visualization within Microsoft Power BI: You need to have competence in explaining the function of dashboards, interactive reports, and paginated reporting, as well as the workflow within Power BI.
  • Explain Data Processing & Ingestion on Azure: The learners should be able to explain the fundamental practices for data loading, Azure Data Factory components, and data processing options.
  • Explain Analytics Workloads: The applicants should be able to explain transactional workloads, data warehousing workloads, and when data warehouse solutions are needed. This subtopic also covers their skills and knowledge associated with the differences between analytics and a transactional workload as well as the differences between real-time and batch.

Microsoft DP-900 Deutsch 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. Structured, semi-structured, and unstructured data
      • 2. File formats
        - Describe data workloads
        • 1. Analytical workloads
          • 2. Transactional workloads
            • 3. Batch and streaming data
              Identify considerations for relational data on Azure20–25%- Describe relational data concepts
              • 1. Normalization
                • 2. Query data with SQL
                  • 3. Relationships, keys, constraints
                    - Describe Azure relational data services
                    • 1. SQL Server on Azure Virtual Machines
                      • 2. Azure SQL Managed Instance
                        • 3. Azure SQL Database
                          • 4. Open-source databases: PostgreSQL, MySQL, MariaDB
                            Describe considerations for working with non-relational data on Azure15–20%- Describe Azure Cosmos DB
                            • 1. APIs and data models
                              • 2. Capabilities and features
                                • 3. Consistency levels and partitioning
                                  - Describe Azure storage services
                                  • 1. Table Storage
                                    • 2. File Storage
                                      • 3. Blob Storage
                                        • 4. Queue Storage
                                          - Describe non-relational data concepts
                                          • 1. Characteristics of NoSQL data
                                            • 2. Use cases for non-relational data
                                              Describe an analytics workload on Azure25–30%- Describe real-time analytics
                                              • 1. Stream processing concepts
                                                • 2. Azure Stream Analytics
                                                  - Describe data visualization
                                                  • 1. Power BI capabilities
                                                    • 2. Data models and reports
                                                      - Describe Azure analytics services
                                                      • 1. Microsoft Fabric
                                                        • 2. Azure Synapse Analytics
                                                          • 3. Azure Data Lake Storage Gen2
                                                            • 4. Azure HDInsight
                                                              • 5. Azure Databricks
                                                                - Describe big data and analytics concepts
                                                                • 1. Data lakes and data warehouses
                                                                  • 2. Modern data warehouse architecture
                                                                    • 3. Data ingestion and processing

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