DP-900: Microsoft Azure Data Fundamental Apr – 2025

Skills at a glance

• Describe core data concepts (25–30%)

• Identify considerations for relational data on Azure (20–25%)

• Describe considerations for working with non-relational data on Azure (15–20%)

• Describe an analytics workload on Azure (25–30%)

Describe core data concepts (25–30%)

Describe ways to represent data

• Describe features of structured data

• Describe features of semi-structured

• Describe features of unstructured data

Identify options for data storage

• Describe common formats for data files

• Describe types of databases

Describe common data workloads

• Describe features of transactional workloads

• Describe features of analytical workloads

Identify roles and responsibilities for data workloads

• Describe responsibilities for database administrators

• Describe responsibilities for data engineers

• Describe responsibilities for data analysts

Identify considerations for relational data on Azure (20–25%)

Describe relational concepts

• Identify features of relational data

• Describe normalization and why it is used

• Identify common structured query language (SQL) statements

• Identify common database objects

Describe relational Azure data services

• Describe the Azure SQL family of products including Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure Virtual Machines

• Identify Azure database services for open-source database systems

Describe considerations for working with non-relational data on Azure (15–20%)

Describe capabilities of Azure storage

• Describe Azure Blob storage

• Describe Azure File storage

• Describe Azure Table storage

Describe capabilities and features of Azure Cosmos DB

• Identify use cases for Azure Cosmos DB

• Describe Azure Cosmos DB APIs

Describe an analytics workload (25–30%)

Describe common elements of large-scale analytics

• Describe considerations for data ingestion and processing

• Describe options for analytical data stores

• Describe Microsoft cloud services for large-scale analytics, including Azure Databricks and Microsoft Fabric

Describe consideration for real-time data analytics

• Describe the difference between batch and streaming data

• Identify Microsoft cloud services for real-time analytics

Describe data visualization in Microsoft Power BI

• Identify capabilities of Power BI

• Describe features of data models in Power BI

• Identify appropriate visualizations for data

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