DP-900 Microsoft Azure Data Fundamentals QA Tests 2026

Are you ready to prepare for the DP-900 Microsoft Azure Data Fundamentals Exam 2026 ?

This exam is an opportunity to demonstrate your knowledge of core data concepts and related Microsoft Azure data services. As a candidate for this exam, you should have familiarity with Exam DP-900’s self-paced or instructor-led learning material.

This exam is intended for you, if you’re a candidate beginning to work with data in the cloud.

You should be familiar with:

• The concepts of relational and non-relational data.

• Different types of data workloads such as transactional or analytical.

You can use Azure Data Fundamentals to prepare for other Azure role-based certifications like Azure Database Administrator Associate or Azure Data Engineer Associate, but it is not a prerequisite for any of them.

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 the features of structured data

• Describe the features of semi-structured data

• Describe the features of unstructured data

Identify options for data storage

• Describe common formats for data files

• Describe features of common data stores including databases

• Identify Azure datastores for common use cases

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 the capabilities of Azure storage

• Describe features of Azure Blob storage

• Describe features of Azure Files

• Describe features of Azure Table storage

Describe the 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 considerations 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 the capabilities of Power BI

• Describe features of data models in Power BI

• Identify appropriate visualizations for data