DP-900 Azure Data Fundamentals Study Guide: Skills Measured and Labs

DP-900 tests whether you can recognise core data concepts and match a workload to the right Azure data service, and you can prepare for it in about three weeks by studying the four domains in Microsoft's skills outline as of July 21, 2026: core data concepts (25-30%), relational data on Azure (20-25%), non-relational data on Azure (15-20%) and analytics workloads (25-30%). Learn the vocabulary, practise basic SQL, open each service once in the Azure portal, and finish with Microsoft's free practice assessment.

Who this is for and what you will have at the end

Microsoft says the exam is for candidates beginning to work with data in the cloud, who should be familiar with relational and non-relational data and with transactional and analytical workloads. It suits students, career changers, business analysts and administrators who need a first Azure data credential. By the end of this guide you will have a domain checklist, a three-week plan, a set of low-cost labs and a decision table for choosing a service.

Exam facts to confirm first

The change log says the July 21, 2026 changes were minor in three places: ways to represent data, the capabilities of Azure storage, and real-time data analytics. The page lists no renewal frequency for this fundamentals certification, so confirm that on the certification page if it matters to you.

Skills measured, domain by domain

Describe core data concepts (25-30%)

• Ways to represent data. Structured, semi-structured and unstructured data.
• Data storage options. Common file formats, features of common data stores including databases, and Azure datastores for common use cases.
• Data workloads. Features of transactional workloads and analytical workloads.
• Roles. Responsibilities of database administrators, data engineers and data analysts.

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

• Relational concepts. Features of relational data, normalization and why it is used, common SQL statements and common database objects.
• Azure relational services. The Azure SQL family (Azure SQL Database, Azure SQL Managed Instance and SQL Server on Azure Virtual Machines) and Azure database services for open-source database systems.

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

• Azure storage. Azure Blob storage, Azure Files and Azure Table storage.
• Azure Cosmos DB. Use cases and the APIs it offers.

Describe an analytics workload (25-30%)

• Large-scale analytics. Data ingestion and processing, analytical data stores, and Microsoft cloud services including Azure Databricks and Microsoft Fabric.
• Real-time analytics. The difference between batch and streaming data and the Microsoft services for real-time analytics.
• Power BI. Capabilities, features of data models and choosing appropriate visualizations.

Quick reference: matching a need to a service

The exam often describes a scenario and asks which service fits. Use this table as a starting framework, and verify details against each service's documentation.

For a deeper comparison of the three SQL options, read Azure SQL Database vs Managed Instance vs SQL Server on a VM.

A three-week study plan

Labs that map to the objectives

Use an Azure free account or a sandbox, and delete resources when you finish. Check each service's current pricing and free offers before creating it, since this guide does not quote them.

1. Read and write basic SQL. Create a database in Azure SQL Database or any SQL Server instance, create a table, then run INSERT, SELECT, UPDATE and DELETE. Create a view and an index so you recognise common database objects.

CREATE TABLE Customers (
    CustomerId INT PRIMARY KEY,
    Name NVARCHAR(100) NOT NULL,
    City NVARCHAR(50)
);
 
INSERT INTO Customers (CustomerId, Name, City)
VALUES (1, 'Contoso Retail', 'Seattle');
 
SELECT Name, City FROM Customers WHERE City = 'Seattle';

2. Normalization. Take a flat spreadsheet of orders with repeated customer details and split it into customers, orders and order lines. Write down what redundancy and update problems normalization removes.
3. Storage tour. Create a storage account and create a blob container, an Azure Files share and a table. Note how each is addressed and what kind of data it suits.
4. Cosmos DB. Create an account, add a container and insert JSON items. Review which APIs the portal offers when you create the account, because the outline asks you to describe them.
5. Formats. Open a CSV, a JSON document and an image. Classify each as structured, semi-structured or unstructured, and list common file formats for analytics that the learning path mentions.
6. Analytics. Read the overview pages for Microsoft Fabric and Azure Databricks and write two sentences on when you would use each. Then draw a batch pipeline and a streaming pipeline side by side.
7. Power BI. Load a small CSV into Power BI Desktop, build a data model with two related tables, and try a bar chart, a line chart and a table to see which visual fits which question.

Troubleshooting your preparation

• Services blur together. Make flash cards with three columns: data shape, typical workload and Azure service.
• SQL looks unfamiliar. You do not need advanced T-SQL, but practise the statement types until you can read them without effort.
• You over-study depth. This is a fundamentals exam. Know what each service is for and how it differs from its neighbours before learning configuration detail.
• Study material mentions Synapse heavily. The July 2026 outline names Azure Databricks and Microsoft Fabric for large-scale analytics, so prioritise those and treat anything else as background.

Final checklist before you book

• You can define structured, semi-structured and unstructured data with an example of each.
• You can explain transactional versus analytical workloads and name the three data roles.
• You can read basic SQL and name common database objects.
• You can choose between the Azure SQL family members and between Blob, Files, Table and Cosmos DB.
• You have completed the free practice assessment and confirmed the exam date, language and price.

References

• Study guide for Exam DP-900: https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-900
• Microsoft Certified: Azure Data Fundamentals: https://learn.microsoft.com/en-us/credentials/certifications/azure-data-fundamentals/
• Azure SQL documentation (listed in the study guide): https://learn.microsoft.com/en-us/azure/azure-sql/
• Azure Cosmos DB documentation (listed in the study guide): https://learn.microsoft.com/en-us/azure/cosmos-db/