Try and practice the latest Microsoft : DP-900 Korean real questions & answers

Last Updated: Sep 02, 2026

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Workload analytics on Azure

  • Elements of a modern data warehouse – this sub-section is focused on Azure data services related to modern data warehousing. In addition, applicants should be familiar with modern data warehousing workload and architecture.
  • Date processing and ingestion on Azure – main topics within this subdomain include data loading common practices, Azure Data Factory components (activities, pipeline, etc.), and data processing.
  • Analytics workloads – under this topic, candidates should learn more about transactional workloads as well as data warehousing workloads. Also, they have to get skilled in differentiating between an analytics and a transactional workload, know the difference between real and batch time, and be able to determining when a data warehouse solution is required.
  • Data visualization in Microsoft Power BI – this topic requires candidates to learn more about paginated reporting, dashboards, interactive reports, as well as workflow in Power BI.

Explain Analytics Workloads on Azure: 25-30%

  • Explain Data Processing & Ingestion on Azure: The learners should be able to explain the fundamental practices for data loading, Azure Data Factory components, and data processing options.
  • Explain Data Visualization within Microsoft Power BI: You need to have competence in explaining the function of dashboards, interactive reports, and paginated reporting, as well as the workflow within Power BI.
  • Explain Analytics Workloads: The applicants should be able to explain transactional workloads, data warehousing workloads, and when data warehouse solutions are needed. This subtopic also covers their skills and knowledge associated with the differences between analytics and a transactional workload as well as the differences between real-time and batch.
  • Explain Modern Data Warehouse Components: The test takers should understand Azure data services for the latest data warehousing, including Azure Synapse Analytics, Azure HDInsight, Azure Data Lake, and Azure Databricks, as well as explainin the workload and architecture for modern data warehousing.

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

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Microsoft DP-900 Korean Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Describe considerations for working with non-relational data on Azure15-20%- Describe non-relational Azure data services
  • 1. Describe Azure Cosmos DB
  • 2. Describe Azure File storage
  • 3. Describe Azure Table storage
  • 4. Describe Azure Blob storage
- Describe non-relational concepts
  • 1. Identify non-relational data
  • 2. Describe non-relational data offerings
Topic 2: Identify considerations for relational data on Azure20-25%- Describe relational concepts
  • 1. Identify features of relational data
  • 2. Describe normalization
- Describe relational Azure data services
  • 1. Describe Azure Database for MySQL
  • 2. Describe Azure SQL Managed Instance
  • 3. Describe Azure Database for PostgreSQL
  • 4. Describe Azure SQL Database
  • 5. Describe Azure Database for MariaDB
  • 6. Describe SQL Server on Azure Virtual Machines
  • 7. Describe Azure SQL family of products
Topic 3: Describe core data concepts25-30%- Describe ways to represent data
  • 1. Describe features of structured data
  • 2. Describe features of unstructured data
  • 3. Describe features of semi-structured data
- Identify options for data storage
  • 1. Describe common formats for data files
  • 2. Describe types of databases
Topic 4: Describe an analytics workload on Azure25-30%- Describe Azure data analytics services
  • 1. Describe Azure Data Factory
  • 2. Describe Azure Stream Analytics
  • 3. Describe Azure HDInsight
  • 4. Describe Microsoft Fabric
  • 5. Describe Azure Synapse Analytics
  • 6. Describe Azure Databricks
- Describe data visualization in Microsoft Power BI
  • 1. Describe Power BI tools and workflow
  • 2. Describe Power BI
  • 3. Describe Power BI reports and dashboards
- Describe analytics concepts
  • 1. Describe data warehousing
  • 2. Describe ETL and ELT processes
  • 3. Describe transactional workloads
  • 4. Describe analytical workloads
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