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

  • Exam Code: DP-900-Deutsch
  • Exam Name: Microsoft Azure Data Fundamentals (DP-900 Deutsch Version)
  • Updated: Aug 06, 2026
  • Q & A: 320 Questions and Answers

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What should you know about the Microsoft DP-900 exam?

The Microsoft DP-900 exam is 60 minutes long and consists of 40-60 questions of various types. Some of these formats may include multiple choice, active screen, drag and drop, build list, best answer, short answer, and case studies, among others. The candidates can find the examples of these question types on the official website. This certification test is designed to evaluate the competence of the professionals in using non-relational and relational data on Azure, as well as the analytics workload that is available on Azure. The exam fee is $99 and the applicants must pay it before registering for and scheduling their test. To complete this exam on the first try, they must achieve at least 720 points on a scale of 100-1000. The languages available for this test include English, German, Korean, Spanish, French, Japanese, and Simplified Chinese.

The Microsoft DP-900 exam is intended for candidates who already have foundational knowledge and skills in performing basic tasks and using Microsoft Azure data services. It is also recommended for those who are starting their careers in the field of cloud data. Although there are no strict prerequisites, every individual who desires to take DP-900 test should have familiarity with non-relational and relational data as well as various types of data workloads such as analytical and transactional.

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

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Microsoft DP-900 Deutsch Exam Syllabus Topics:

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

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