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

DP-900 Deutsch

Exam Code: DP-900-Deutsch

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

Updated: Aug 08, 2026

Q & A: 320 Questions and Answers

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About Microsoft DP-900 Deutsch Exam

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Languages: English, Japanese, Chinese (Simplified), Korean, French, German, Spanish, Portuguese (Brazil), Russian, Indonesian (Indonesia), Arabic (Saudi Arabia), Chinese (Traditional), Italian

Retirement date: none

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.

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

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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 Deutsch Exam Syllabus Topics:

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

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