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  • Exam Code: DP-900J
  • Exam Name: Microsoft Azure Data Fundamentals (DP-900日本語版)
  • Last Updated: Aug 15, 2026
  • Q & A: 332 Questions and Answers
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  • Exam Name: Microsoft Azure Data Fundamentals (DP-900日本語版)
  • Last Updated: Aug 15, 2026
  • Q & A: 332 Questions and Answers
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About Microsoft Azure Data Fundamentals (DP-900日本語版) : DP-900日本語 Exam

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Languages: English, Japanese, Chinese (Simplified), Korean, French, German, Spanish, Portuguese (Brazil), Russian, Indonesian (Indonesia), Arabic (Saudi Arabia), Chinese (Traditional), Italian

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Prove that you can describe the following: core data concepts; how to work with relational data on Azure; how to work with non-relational data on Azure; and an analytics workload on Azure.

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What requirements should you fulfill before taking the Microsoft DP-900 exam?

The candidates for this certification exam are those individuals who want to validate their foundational-level experience in working with data within Cloud. They should possess the basic knowledge and skills in the domain of core data concepts. They also need to have an understanding of how these are implemented with the use of Microsoft Azure data services.

The applicants do not have to fulfill any requirements to take the Microsoft DP-900 test or get the associated certification. However, they must be familiar with different concepts of non-relational and relational data. They should also know various types of data workloads that are available, including analytical and transactional. Additionally, the students must develop mastery of the exam topics before sitting for this test.

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

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Microsoft DP-900日本語 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Describe core data concepts25–30%- 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 data workloads
        • 1. Analytical workloads
          • 2. Batch and streaming data
            • 3. Transactional workloads
              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. Consistency levels and partitioning
                    • 2. APIs and data models
                      • 3. Capabilities and features
                        - Describe Azure storage services
                        • 1. File Storage
                          • 2. Queue Storage
                            • 3. Table Storage
                              • 4. Blob Storage
                                Topic 3: Identify considerations for relational data on Azure20–25%- Describe Azure relational data services
                                • 1. SQL Server on Azure Virtual Machines
                                  • 2. Azure SQL Managed Instance
                                    • 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 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 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. Microsoft Fabric
                                                              • 2. Azure Synapse Analytics
                                                                • 3. Azure Data Lake Storage Gen2
                                                                  • 4. Azure HDInsight
                                                                    • 5. Azure Databricks

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