Microsoft Azure Data Fundamentals : DP-900

  • Exam Code: DP-900
  • Exam Name: Microsoft Azure Data Fundamentals
  • Updated: Aug 20, 2026
  • Q&As: 332 Questions and Answers

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About Microsoft DP-900 Real Exam

Career Prospects

  • Systems Administrator – as a sysadmin, you will be tasked with providing hands-on support when it comes to system maintenance, operation and administration. You will also be the person concerned in making recommendation of an appropriate software to be deployed. The average salary is estimated to be $63k.
  • Systems Engineer – this role will keep you engaged in the design and the development of the cloud solutions that are appropriate to the business at hand. These professionals also analyze both the system and software requirements before giving design guidelines. This role is estimated to earn you an average salary of $81k per annum, as per PayScale.
  • Cloud Solutions Analyst – an early career as cloud solutions analyst or architect is quite lucrative. You can earn up to an average salary of $80k annually with even less than 1 year of working experience in this role. Once you develop in this job, the salary may rise up to the average of $126k per annum. Although the role is so much demanding, you should be ready to do proper evaluation and performance testing of a cloud solution before integration. The main task of these specialists is to ensure that the performance of the system is optimized.

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

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DP-900 exam dumps

Workload analytics on Azure

  • Elements of a modern data warehouse – this sub-section is focused on Azure data services related to modern data warehousing. In addition, applicants should be familiar with modern data warehousing workload and architecture.
  • Data visualization in Microsoft Power BI – this topic requires candidates to learn more about paginated reporting, dashboards, interactive reports, as well as workflow in Power BI.
  • Analytics workloads – under this topic, candidates should learn more about transactional workloads as well as data warehousing workloads. Also, they have to get skilled in differentiating between an analytics and a transactional workload, know the difference between real and batch time, and be able to determining when a data warehouse solution is required.
  • Date processing and ingestion on Azure – main topics within this subdomain include data loading common practices, Azure Data Factory components (activities, pipeline, etc.), and data processing.

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Microsoft DP-900 Exam Syllabus Topics:

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

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