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Microsoft DP-900日本語 real answers - Microsoft Azure Data Fundamentals (DP-900日本語版)

DP-900日本語
  • Exam Code: DP-900J
  • Exam Name: Microsoft Azure Data Fundamentals (DP-900日本語版)
  • Updated: Sep 15, 2026
  • Q & A: 332 Questions and Answers
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How to operate with non-relational data on Azure

The main concepts covered in this domain include:

  • Non relation data workloads – this sub-section mostly emphasizes the features of non-relational data, when to use it, one's knowledge of the variants of NoSQL and non-relational data, and the ability to choose and recommend appropriate data store.
  • Core administration tasks – this sub-domain covers the processes of deployment and provisioning when it comes to non-relational data, data security, diverse tools for management, and defining the issues with connectivity.
  • Offerings non-relational data on Azure – within this topic, the learner will be conversant with the following in Azure: data services, Cosmos DB APIs, Table storage, Blob storage, File storage.

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

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The Microsoft DP-900 (Microsoft Azure Data Fundamentals) is the single exam students will have to overcome if they want to attain the Microsoft Certified: Azure Data Fundamentals certificate. Those who hold this certification can become a developer, data engineer, data administrator, or data analyst.

Microsoft DP-900日本語 Exam Syllabus Topics:

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

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