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Microsoft Azure Data Fundamentals : DP-900 Exam

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

Explain Analytics Workloads on Azure: 25-30%

  • 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 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 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.
  • 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.

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

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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.

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DP-900 exam dumps

Microsoft DP-900 Exam Syllabus Topics:

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

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