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Last Updated: Sep 02, 2026

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The Microsoft DP-900 exam is intended for candidates who already have foundational knowledge and skills in performing basic tasks and using Microsoft Azure data services. It is also recommended for those who are starting their careers in the field of cloud data. Although there are no strict prerequisites, every individual who desires to take DP-900 test should have familiarity with non-relational and relational data as well as various types of data workloads such as analytical and transactional.

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Workload analytics on Azure

  • 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.
  • 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.
  • 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.

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

Microsoft DP-900 Korean Exam Syllabus Topics:

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