Microsoft DP-900 Deutsch Valid Q&A - in .pdf

  • DP-900 Deutsch pdf
  • Exam Code: DP-900 Deutsch
  • Exam Name: Microsoft Azure Data Fundamentals (DP-900 Deutsch Version)
  • Updated: Aug 31, 2026
  • Q & A: 332 Questions and Answers
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  • Exam Code: DP-900 Deutsch
  • Exam Name: Microsoft Azure Data Fundamentals (DP-900 Deutsch Version)
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  • Updated: Aug 31, 2026
  • Q & A: 332 Questions and Answers
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Microsoft DP-900 Deutsch Valid Q&A - Testing Engine

  • DP-900 Deutsch Testing Engine
  • Exam Code: DP-900 Deutsch
  • Exam Name: Microsoft Azure Data Fundamentals (DP-900 Deutsch Version)
  • Updated: Aug 31, 2026
  • Q & A: 332 Questions and Answers
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Certification Paths Available

Upon earning Microsoft Certified: Azure Data Fundamentals, you will be able to advance your career to higher-level certifications. For instance, it can be a starting point for Microsoft Certified: Azure Data Engineer Associate or Microsoft Certified: Azure Database Administrator Associate. However, this fundamental accreditation is not a requirement for earning any of the next certificates. Therefore, all you have to do is to pass Microsoft DP-900 exam and prepare to face the real-world challenges as you advance in your career.

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

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Microsoft DP-900 Practice Test Questions, Microsoft DP-900 Exam Practice Test Questions

The Microsoft DP-900 exam is a qualifying test for obtaining the Microsoft Certified: Azure Data Fundamentals certification. This exam is intended for the entry-level professionals looking to take up a career path in the IT field. Although it is not a requirement for any advanced-level certificate, the DP-900: Microsoft Azure Data Fundamentals test is a great foundation to prepare the individuals for other Azure role-based options. Thus, the certified candidates can pursue two certifications: Azure Data Engineer Associate or Azure Database Administrator Associate.

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Microsoft DP-900 Deutsch Exam Syllabus Topics:

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