Prepare for the DP-900 exam with confidence! This set includes 230 unique practice questions created from scratch and fully compliant with the official 2025 exam syllabus.

The DP-900: Microsoft Azure Data Fundamentals exam syllabus is structured around four main domains, covering core data concepts and how they are implemented using Microsoft Azure data services.

Domain Approximate Weighting

1. Describe core data concepts 25−30%

2. Identify considerations for relational data on Azure 20−25%

3. Describe considerations for working with non-relational data on Azure 15−20%

4. Describe an analytics workload on Azure 25−30%

1. Describe Core Data Concepts (25−30%)

Describe ways to represent data:
- Features of structured, semi-structured, and unstructured data.
Identify options for data storage:
- Common formats for data files.
- Types of databases (e.g., relational, non-relational).
Describe common data workloads:
- Features of transactional (OLTP) workloads.
- Features of analytical (OLAP) workloads.
Identify roles and responsibilities for data workloads:
- Responsibilities for Database Administrators, Data Engineers, and Data Analysts.

2. Identify Considerations for Relational Data on Azure (20−25%)

Describe relational concepts:
- Features of relational data (tables, columns, rows).
- Normalization and why it is used.
- Common SQL statements (DDL and DML).
- Common database objects (tables, views, stored procedures).
Describe relational Azure data services:
- The Azure SQL family of products (Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure Virtual Machines).
- Azure database services for open-source database systems (e.g., Azure Database for PostgreSQL, Azure Database for MySQL).

3. Describe Considerations for Working with Non-Relational Data on Azure (15−20%)

Describe capabilities of Azure storage:
- Azure Blob storage.
- Azure File storage.
- Azure Table storage.
Describe capabilities and features of Azure Cosmos DB:
- Identify use cases for Azure Cosmos DB (globally distributed, multi-model).
- Describe Azure Cosmos DB APIs (e.g., SQL, MongoDB, Cassandra).

4. Describe an Analytics Workload on Azure (25−30%)

Describe common elements of large-scale analytics:
- Considerations for data ingestion and processing (ETL/ELT).
- Options for analytical data stores (e.g., Data Lakes, Data Warehouses).
- Microsoft cloud services for large-scale analytics, including Azure Synapse Analytics and Azure Databricks.
Describe consideration for real-time data analytics:
- Difference between batch and streaming data.
- Technologies for real-time analytics (e.g., Azure Stream Analytics).
Describe data visualization in Microsoft Power BI:
- Identify capabilities of Power BI (interactive reports, dashboards).
- Describe features of data models in Power BI.