Microsoft Azure Data Fundamentals (DP-900日本語版) : DP-900日本語 test torrent

DP-900日本語 Exam Simulator
  • Exam Code: DP-900J
  • Exam Name: Microsoft Azure Data Fundamentals (DP-900日本語版)
  • Updated: Jul 25, 2026
  • Q & A: 320 Questions and Answers

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Explain the Techniques Involved in Working with Relational Data on Microsoft Azure: 25-30%

  • Explain Relational Azure Data Services: The individuals should have the ability to explain and compare SaaS, PaaS, and IaaS solutions as well as Azure Synapse Analytics. They also need the knowledge of Azure SQL database services, Azure Database for PostgreSQL, Azure Database for MySQL, and Azure Database for MariaDB.
  • Identify the Fundamental Management Tasks for Relational Data: The applicants should be able to explain the deployment and provisioning of relational data services. They also need to understand the techniques for deployment, such as Azure Resource Manager templates, Azure portal, Azure CLI, and Azure PowerShell. In addition, the students should be able to identify the fundamental connectivity issues and components of data security.
  • Explain the Concepts of Relational Data Workloads: The examinees should be able to explain the structures of relational data and identify the appropriate data for relational workloads.
  • Explain the Query Methods for Data with the SQL Language: The learners should be capable of comparing the Data Manipulation Language with the Data Definition Language. They should also be able to query relational data within Azure Database for MySQL, Azure SQL Database, and Azure Database for PostgreSQL.

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The Microsoft DP-900 test evaluates the ability of the candidates to execute specific technical tasks. The examinees must understand these areas before they take this test. The topics that this certification exam covers are as follows:

Explain the Concepts of Core Data: 15-20%

  • Explain the Core Concepts of Data Analytics: The candidates need to be able to explain data visualization, analytics techniques, data processing concepts, ETL and ELT processing, as well as the basic types of charts, including pie charts and bar charts.
  • Explain Core Data Workloads Types: The test takers should be able to explain batch data, attributes of relational data, streaming data, and the differences between streaming and batch data.

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

General Overview of Microsoft DP-900 Exam

The Microsoft DP-900 exam evaluates the individuals’ understanding of core data concepts, analytics workloads on Azure, as well as how to function with relational and non-relational data within Azure. The test contains 40-60 questions and the applicants have 90 minutes for the completion. The students or instructors who want to take this exam are required to schedule it through Certiport while the non-students are to schedule the test with Pearson VUE. The fee for the exam is $99 and it is available in a range of languages, including English, Spanish, French, Korean, German, Japanese, and Simplified Chinese.

Microsoft DP-900 Exam Syllabus Topics:

TopicDetails

Describe core data concepts (15-20%)

Describe types of core data workloads- describe batch data
- describe streaming data
- describe the difference between batch and streaming data
- describe the characteristics of relational data
Describe data analytics core concepts- describe data visualization (e.g., visualization, reporting, business intelligence (BI))
- describe basic chart types such as bar charts and pie charts
- describe analytics techniques (e.g., descriptive, diagnostic, predictive, prescriptive, cognitive)
- describe ELT and ETL processing
- describe the concepts of data processing

Describe how to work with relational data on Azure (25-30%)

Describe relational data workloads- identify the right data offering for a relational workload
- describe relational data structures (e.g., tables, index, views)
Describe relational Azure data services- describe and compare PaaS, IaaS, and SaaS solutions
- describe Azure SQL family of products including Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure Virtual Machines
- describe Azure Synapse Analytics
- describe Azure Database for PostgreSQL, Azure Database for MariaDB, and Azure Database for MySQL
Identify basic management tasks for relational data- describe provisioning and deployment of relational data services
- describe method for deployment including the Azure portal, Azure Resource Manager templates, Azure PowerShell, and the Azure command-line interface (CLI)
- identify data security components (e.g., firewall, authentication)
- identify basic connectivity issues (e.g., accessing from on-premises, access with Azure VNets, access from Internet, authentication, firewalls)
- identify query tools (e.g., Azure Data Studio, SQL Server Management Studio, sqlcmd utility, etc.)
Describe query techniques for data using SQL language- compare Data Definition Language (DDL) versus Data Manipulation Language (DML)
- query relational data in Azure SQL Database, Azure Database for PostgreSQL, and Azure Database for MySQL

Describe how to work with non-relational data on Azure (25-30%)

Describe non-relational data workloads- describe the characteristics of non-relational data
- describe the types of non-relational and NoSQL data
- recommend the correct data store
- determine when to use non-relational data
Describe non-relational data offerings on Azure- identify Azure data services for non-relational workloads
- describe Azure Cosmos DB APIs
- describe Azure Table storage
- describe Azure Blob storage
- describe Azure File storage
Identify basic management tasks for non-relational data- describe provisioning and deployment of non-relational data services
- describe method for deployment including the Azure portal, Azure Resource Manager templates, Azure PowerShell, and the Azure command-line interface (CLI)
- identify data security components (e.g., firewall, authentication, encryption)
- identify basic connectivity issues (e.g., accessing from on-premises, access with Azure VNets, access from Internet, authentication, firewalls)
- identify management tools for non-relational data

Describe an analytics workload on Azure (25-30%)

Describe analytics workloads- describe transactional workloads
- describe the difference between a transactional and an analytics workload
- describe the difference between batch and real time
- describe data warehousing workloads
- determine when a data warehouse solution is needed
Describe the components of a modern data warehouse- describe Azure data services for modern data warehousing such as Azure Data Lake Storage Gen2, Azure Synapse Analytics, Azure Databricks, and Azure HDInsight
- describe modern data warehousing architecture and workload
Describe data ingestion and processing on Azure- describe common practices for data loading
- describe the components of Azure Data Factory (e.g., pipeline, activities, etc.)
- describe data processing options (e.g., Azure HDInsight, Azure Databricks, Azure Synapse Analytics, Azure Data Factory)
Describe data visualization in Microsoft Power BI- describe the role of paginated reporting
- describe the role of interactive reports
- describe the role of dashboards
- describe the workflow in Power BI

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

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