Microsoft DP-900J exam dumps : Microsoft Azure Data Fundamentals (DP-900日本語版)

  • Exam Code: DP-900J
  • Exam Name: Microsoft Azure Data Fundamentals (DP-900日本語版)
  • Updated: Sep 17, 2026     Q & A: 332 Questions and Answers

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About Microsoft DP-900日本語 Exam

To pass the DP-900 exam, candidates need to have a good understanding of the core concepts of data storage, processing, and security. They also need to be familiar with the Azure services and tools that are used for data management and processing. DP-900J exam consists of multiple-choice questions and is designed to be completed within 60 minutes.

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The DP-900 exam is a great starting point for individuals who want to pursue a career in data analytics or data engineering. It covers the foundational knowledge required to work with Azure data services and provides a solid foundation for more advanced certifications in the field.

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

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Microsoft DP-900 exam, also known as the Microsoft Azure Data Fundamentals exam, is a certification exam that tests the candidate's knowledge and understanding of core data concepts and how they are implemented in Microsoft Azure. DP-900J exam is designed for individuals who have a basic understanding of data concepts and are looking to validate their understanding of Azure data services. DP-900J exam covers a wide range of topics, including core data concepts, relational data services, non-relational data services, analytics services, and Azure data services.

Microsoft DP-900日本語 Exam Syllabus Topics:

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

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