Microsoft Azure Data FundamentalsStudy guide
The fundamentals certification for Azure data (DP-900).
About Microsoft Azure Data Fundamentals (DP-900)
Microsoft Azure Data Fundamentals (DP-900) is a Fundamentals-level certification from Microsoft. This page organizes the exam scope into a 4-chapter, 10-section study guide and lets you check your understanding with exam-style practice questions. A good flow is to read the chapters below in order, then test yourself via "Practice questions."
Exam domains (approximate weighting)
- Core data concepts~28%
- Relational data on Azure~22%
- Non-relational data on Azure~20%
- Analytics workloads on Azure~30%
Weights are approximate guidance for the live exam. Each domain is covered in detail in the chapters and sections below.
Official exam information: https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-900
1Core Data Concepts
- 1.1Types of Data and Data Roles
Understand the three kinds of data (structured, semi-structured, unstructured), the stores suited to each, and the main data roles (database administrator, data engineer, data analyst). The starting point for DP-900.
- 1.2Transactional and Analytical Workloads (OLTP and OLAP)
Understand the two major data workload categories—transactional (OLTP) and analytical (OLAP)—plus ACID properties and how data warehouses and ETL/ELT relate.
2Relational Data on Azure
- 2.1Relational Data Concepts
Understand relational data fundamentals—tables, rows, columns, data types, primary/foreign keys, indexes, views—plus normalization and the four SQL categories (DDL/DML/DQL/DCL).
- 2.2Azure Relational Data Services
Understand Azure’s relational services—Azure SQL Database, SQL Managed Instance, SQL Server on VM, Azure Database for PostgreSQL/MySQL—plus the IaaS vs PaaS responsibility split and how to choose.
3Non-relational Data on Azure
- 3.1Non-relational Data and Azure Cosmos DB
Understand the four kinds of non-relational (NoSQL) data (key-value, document, column-family, graph), how they differ from relational, and the features, multiple APIs, and global distribution of Azure Cosmos DB.
- 3.2Azure Storage Services
Understand the Blob, File, Table, and Queue services of an Azure storage account, Blob access tiers, and their uses.
4Analytics Workloads on Azure
- 4.1The Analytics Pipeline, Data Warehouse, and Data Lake
Understand the typical analytics pipeline (ingest → process → store → visualize), the difference between a data warehouse and a data lake, and ETL/ELT.
- 4.2Azure Analytics Services
Understand the Azure services behind the analytics pipeline—Azure Data Factory, Azure Synapse Analytics, Azure Databricks, and Azure Stream Analytics—and their roles.
- 4.3Power BI and Batch vs. Stream Processing
Understand data visualization with Microsoft Power BI (reports/dashboards) and the difference between batch and stream processing.
- 4.4Microsoft Fabric (Unified Analytics Platform)
Understand Microsoft Fabric, which became the center of the DP-900 analytics domain in the 2026 update. Fabric is a SaaS unified analytics platform bundling ingestion, data engineering, warehousing, real-time analytics, data science, and Power BI—all running on a single data lake, OneLake.