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

Microsoft DP-900日本語 test insides dumps
  • Exam Code: DP-900J
  • Exam Name: Microsoft Azure Data Fundamentals (DP-900日本語版)
  • Updated: Sep 19, 2026
  • Q & A: 332 Questions and Answers
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About Microsoft DP-900日本語 Testinsides IT real test

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What should you know about the Microsoft DP-900 exam?

The Microsoft DP-900 exam is 60 minutes long and consists of 40-60 questions of various types. Some of these formats may include multiple choice, active screen, drag and drop, build list, best answer, short answer, and case studies, among others. The candidates can find the examples of these question types on the official website. This certification test is designed to evaluate the competence of the professionals in using non-relational and relational data on Azure, as well as the analytics workload that is available on Azure. The exam fee is $99 and the applicants must pay it before registering for and scheduling their test. To complete this exam on the first try, they must achieve at least 720 points on a scale of 100-1000. The languages available for this test include English, German, Korean, Spanish, French, Japanese, and Simplified Chinese.

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Preparation Process for Microsoft DP-900 Exam

The learners can use different training options that Microsoft offers to prepare for the DP-900 test. The first step in your preparation process is to go over the exam objectives in detail. They can be found on the official website. The resources to help understand these topics are also available on the vendor’s webpage. Microsoft comes with a paid instructor-led training as well as free learning paths as the best prep options for your certification test.

  • Study Guide

    The Exam Ref DP-900: Microsoft Azure Data Fundamentals book is another good resource for preparation. Authored by Daniel A. Seara, Francesco Milano, this study guide covers the in-depth analysis of the topics and offers some practical skills that will help you take the test. The book also comes with the sample exam questions to help you ace your test.

  • Online Learning Paths

    This option of learning is free and made up of seven paths with different modules for each. The candidates for this option are those with the ability to study on their own. The self-paced learning paths offer a comprehensive outline and lessons on the topics of the exam.

  • Practice Test

    The official practice test is also available to help the examinees gain confidence in their test-taking skills. The candidates can use it to evaluate their readiness for the real exam. They can also get familiar with the pattern of the test and develop competence in answering the exam questions. The official practice test can be found on the Microsoft website.

  • Instructor-Led Training

    This is a paid training course that is available in-classroom or as virtual classes. The Microsoft Azure Data Fundamentals course is designed to teach the candidates the fundamentals of the concepts of database in the Cloud environments. The students will be equipped with the basic skills in the domain of Cloud data services. They will also be able to develop foundational knowledge and skills in Cloud data services within Microsoft Azure.

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

Microsoft DP-900日本語 Exam Syllabus Topics:

SectionWeightObjectives
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. Transactional workloads
          • 2. Analytical workloads
            • 3. Batch and streaming data
              Identify considerations for relational data on Azure20–25%- Describe relational data concepts
              • 1. Normalization
                • 2. Query data with SQL
                  • 3. Relationships, keys, constraints
                    - Describe Azure relational data services
                    • 1. SQL Server on Azure Virtual Machines
                      • 2. Azure SQL Managed Instance
                        • 3. Open-source databases: PostgreSQL, MySQL, MariaDB
                          • 4. Azure SQL Database
                            Describe an analytics workload on Azure25–30%- Describe big data and analytics concepts
                            • 1. Modern data warehouse architecture
                              • 2. Data ingestion and processing
                                • 3. Data lakes and data warehouses
                                  - 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. Azure Data Lake Storage Gen2
                                            • 2. Azure Databricks
                                              • 3. Azure Synapse Analytics
                                                • 4. Azure HDInsight
                                                  • 5. Microsoft Fabric
                                                    Describe considerations for working with non-relational data on Azure15–20%- Describe Azure storage services
                                                    • 1. Blob Storage
                                                      • 2. Queue Storage
                                                        • 3. Table Storage
                                                          • 4. File Storage
                                                            - Describe Azure Cosmos DB
                                                            • 1. Consistency levels and partitioning
                                                              • 2. Capabilities and features
                                                                • 3. APIs and data models
                                                                  - Describe non-relational data concepts
                                                                  • 1. Use cases for non-relational data
                                                                    • 2. Characteristics of NoSQL data

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