Exam Quick Facts
| Detail | Value |
|---|---|
| Exam Code | DP-900 |
| Title | Microsoft Azure Data Fundamentals |
| Level | Fundamentals |
| Pass Score | 700 / 1000 |
| Duration | 45 minutes |
| Questions | ~40–60 (multiple choice, drag-and-drop) |
| Cost | $99 USD (varies by region) |
| Scheduling | Pearson VUE / Certiport (students) |
| Skills Updated | November 1, 2024 |
Official Learning Paths
Complete these four Microsoft Learn paths to cover the full syllabus:
- 📘 Azure Data Fundamentals: Explore core data concepts — Data formats, databases, transactional vs analytical workloads, data roles
- 📘 Azure Data Fundamentals: Explore relational data in Azure — Relational concepts, SQL, normalization, Azure SQL and open-source databases
- 📘 Azure Data Fundamentals: Explore non-relational data in Azure — Azure Storage services, Azure Cosmos DB, and NoSQL data models
- 📘 Azure Data Fundamentals: Explore data analytics in Azure — Data warehousing, data ingestion, real-time analytics, and Power BI
📖 Study Resources
| Resource | Link |
|---|---|
| 📝 Official Exam Page | Microsoft Learn — DP-900 |
| 📖 Official Study Guide | Microsoft Study Guide |
| 🎯 Free Practice Assessment | Start Practice Assessment |
| 🖥️ Exam Sandbox | Try the exam interface |
| 🎬 Exam Readiness Zone | Video prep series |
Skills at a Glance
| Skill Area | Weight |
|---|---|
| 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% |
Who is this exam for?
The DP-900 is designed for anyone looking to understand the basics of data in the cloud — whether you’re an IT professional exploring data roles, a business decision-maker, or a student just getting started. It covers core data concepts like relational and non-relational databases, analytics workloads, and Power BI — all in the context of Microsoft Azure.
You don’t need any coding experience or hands-on Azure data work to pass this exam, but a basic understanding of what data is and how organisations use it will help you get up to speed faster.
Skills Measured — with Microsoft Learn Links
Every objective below links directly to the Microsoft Learn page that covers it. Click any link to dive into that topic. The objectives are grouped into four domains, weighted by how much of the exam they represent.
Describe core data concepts (25–30%)
This is one of two equally weighted top domains on the exam. It builds the foundation for everything else — how data is structured, where it lives, and who works with it. If you’re new to data concepts, start here and make sure you’re comfortable before moving on.
Describe ways to represent data
Data comes in different shapes. Structured data fits neatly into tables (think spreadsheets), semi-structured data has some organisation but is more flexible (like JSON or XML), and unstructured data has no fixed format at all (images, videos, emails). The exam will test whether you can identify and distinguish between these types.
- Describe features of structured data
- Describe features of semi-structured data
- Describe features of unstructured data
Identify options for data storage
Once you understand the types of data, you need to know where to store them. This section covers common file formats (CSV, JSON, Parquet, Avro) and the main types of databases — relational and non-relational. Know the difference and when you’d choose each.
Describe common data workloads
Data workloads generally fall into two categories: transactional (recording day-to-day operations, like orders and payments) and analytical (analysing historical data to find trends and insights). Understanding the difference is key — the exam loves asking scenario questions about which workload type applies.
Identify roles and responsibilities for data workloads
Three key roles show up in data teams: database administrators (keep databases running and secure), data engineers (build the pipelines that move and transform data), and data analysts (turn data into insights and reports). Know what each role does — the exam tests whether you can match responsibilities to the right job title.
- Describe responsibilities for database administrators
- Describe responsibilities for data engineers
- Describe responsibilities for data analysts
Identify considerations for relational data on Azure (20–25%)
This domain focuses on traditional relational databases — the kind built on tables, rows, and columns. You’ll need to understand core concepts like normalization and SQL, plus the Azure services that host relational databases. If you’ve ever used a spreadsheet, you already have the right mental model — relational databases take that idea further.
Describe relational concepts
Relational data is organised into tables where each row is a record and each column is a field. This section covers how tables relate to each other, why normalization helps reduce duplicated data, the basics of SQL (SELECT, INSERT, UPDATE, DELETE), and common database objects like views, indexes, and stored procedures.
- 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
Azure offers several managed relational database services. The Azure SQL family (Azure SQL Database, Azure SQL Managed Instance, SQL Server on Azure VMs) covers Microsoft’s own database engine, while open-source options include Azure Database for MySQL, PostgreSQL, and MariaDB. Know the key differences and when you’d choose each.
- Describe the Azure SQL family of products
- Identify Azure database services for open-source database systems
Describe considerations for working with non-relational data on Azure (15–20%)
This is the smallest domain by weight, but don’t skip it. Non-relational (NoSQL) data is everywhere — from blob storage holding images and documents to Cosmos DB powering globally distributed apps. The exam tests whether you know which Azure storage option to use for different types of unstructured and semi-structured data.
Describe capabilities of Azure storage
Azure Storage provides several services for different data needs. Blob storage handles unstructured data like images, videos, and backups. Azure Files offers fully managed file shares accessible via SMB and NFS. Table storage provides a simple key-value NoSQL store for structured, non-relational data. Know the use case for each.
Describe capabilities and features of Azure Cosmos DB
Azure Cosmos DB is Microsoft’s globally distributed, multi-model NoSQL database. It’s designed for low-latency, highly available applications that need to work at planet-scale. You’ll need to know common use cases (IoT, e-commerce, gaming) and the different APIs it supports (NoSQL, MongoDB, Cassandra, Gremlin, Table).
Describe an analytics workload on Azure (25–30%)
This is the other top-weighted domain, tied with core data concepts. It covers how organisations process and analyse large volumes of data — from data warehouses and ingestion pipelines to real-time streaming and Power BI visualisations. This domain plus core data concepts make up over half the exam — focus your study time here.
Describe common elements of large-scale analytics
Modern analytics involves moving data from many sources into a central store, transforming it along the way, and then making it available for reporting. This section covers data ingestion, data warehouses, data lakehouses, and cloud services like Azure Synapse Analytics, Azure Databricks, and Microsoft Fabric that bring it all together.
- Describe considerations for data ingestion and processing
- Describe options for analytical data stores
- Describe Microsoft cloud services for large-scale analytics
Describe consideration for real-time data analytics
Not all data can wait for a nightly batch job. Streaming data (from IoT sensors, social media, financial transactions) needs to be processed as it arrives. This section covers the difference between batch and streaming processing, plus Azure services like Azure Stream Analytics and Apache Spark that handle real-time data.
- Describe the difference between batch and streaming data
- Identify Microsoft cloud services for real-time analytics
Describe data visualization in Microsoft Power BI
Power BI is Microsoft’s business intelligence tool that turns raw data into interactive dashboards and reports. You’ll need to know the core components (Power BI Desktop, Service, and Mobile), how data models work (tables, relationships, measures), and when to use different visualization types (bar charts, line charts, maps, treemaps, etc.).
- Identify capabilities of Power BI
- Describe features of data models in Power BI
- Identify appropriate visualizations for data