Microsoft Certified: Azure Data Fundamentals (DP-900) is the first gate in the Azure data certification lineup. It is not a test of writing fluent SQL or building pipelines. It is a Fundamentals-level certification that checks whether you can tell what shape a piece of data has and which storage and analytics services suit it, so you can take it on without hands-on data experience.
This post is the guide to our 15-part DP-900 domain deep-dive series. Before getting into individual topics, it maps out the exam structure, who should take it, and where this certification sits on the Azure career path.
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Who Is DP-900 For?
DP-900 does not ask whether you can tune a database yourself. It asks whether you know where each kind of data is kept and how it is retrieved. You do not have to be a librarian to find what you need, as long as you know that novels are on the shelves, newspapers are in the periodicals room, and maps are in the reference room.
This certification is a particularly good fit for the following people.
| Target Audience | Why It Fits | |------|------| | Non-technical roles (planning, marketing, sales, finance) | Follow database and analytics terminology accurately in data project discussions | | Data analysts and BI beginners | See the full structure of the storage and analytics services behind Power BI | | Developers and IT practitioners | Get clear criteria for choosing between relational and non-relational stores | | People moving into data roles | Groundwork before role-based certifications such as DP-300 | | Managers and decision makers | Tell what each service is for when a data platform is being evaluated |
There are no prerequisite certifications or required courses, and Fundamentals certifications do not expire. Microsoft notes that this certification helps you prepare for role-based certifications such as Azure Database Administrator Associate, but it is not a prerequisite for any of them.
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Exam at a Glance
| Item | Detail | |------|------| | Exam Code | DP-900 | | Full Name | Microsoft Certified: Azure Data Fundamentals | | Level | Fundamentals (beginner) | | Passing Score | 700 (out of 1,000) | | Duration | 45 minutes (65 minutes of seat time including instructions and agreements) | | Questions | Varies (Microsoft does not publish an exact question count) | | Exam Fee | Varies by country ($99 USD in the United States) | | Validity | Does not expire (no renewal needed) | | Languages | 13 languages, including English | | Delivery | Pearson VUE test center or online proctored exam | | Prerequisites | None |
With only 45 minutes, you cannot afford to linger on any single question. The safe approach is to mark a confusing question for review, move on, and come back at the end. If you fail, you can retake the exam after 24 hours; every attempt after that requires a 14-day wait, with a maximum of five attempts in a 12-month period.
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Domain Breakdown
DP-900 is made up of four domains, and the weight of each is published as a range. The breakdown below follows the official skills outline as revised on July 21, 2026.
| Domain | Topic | Weight | Deep dives | |------|------|------|------| | Domain 1 | Describe core data concepts | 25–30% | 3 posts | | Domain 2 | Identify considerations for relational data on Azure | 20–25% | 3 posts | | Domain 3 | Describe considerations for working with non-relational data on Azure | 15–20% | 3 posts | | Domain 4 | Describe an analytics workload on Azure | 25–30% | 6 posts |
Domains 1 and 4 carry the most weight at 25–30% each. Domain 1 covers the concepts the rest of the exam is measured against, such as structured, semi-structured, and unstructured data and transactional versus analytical workloads; Domain 4 covers large-scale analytics, real-time analytics, and Power BI. The current outline names Azure Databricks and Microsoft Fabric explicitly, so if you are studying from older material, make sure you fill that gap.
!DP-900 exam domain weight breakdown across 4 domains
Domain 1: Describe core data concepts (25–30%)
This domain covers the shapes data takes, how it is stored, the types of workloads, and the data roles. It tests how you classify things rather than service names.
Key topics: Structured, semi-structured, and unstructured data; data file formats such as CSV, JSON, and Parquet; types of databases; transactional (OLTP) and analytical (OLAP) workloads; the responsibilities of database administrators, data engineers, and data analysts.
Exam tip: "Lots of fast, accurate reads and writes, like orders and payments" → transactional workload (OLTP). "Aggregate large volumes of historical data to see trends" → analytical workload (OLAP). JSON is semi-structured; images and video are unstructured.
Related posts: Core Data Concepts, Data Workloads and Roles, Choosing the Right Azure Data Store
Domain 2: Identify considerations for relational data on Azure (20–25%)
This domain covers data organized into tables and relationships, and the relational database services on Azure.
Key topics: Features of relational data; normalization; common SQL statements; database objects such as views and indexes; Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure Virtual Machines; the Azure Database services for MySQL and PostgreSQL.
Exam tip: "Move an existing SQL Server almost as is, but hand off the management" → Azure SQL Managed Instance. "We need control all the way down to the operating system" → SQL Server on Azure Virtual Machines. "A fully managed database for a new cloud app" → Azure SQL Database.
Related posts: Relational Data Fundamentals, SQL Statement Types (DDL, DML, DCL) and Constraints, Azure Data Security Basics
Domain 3: Describe considerations for working with non-relational data on Azure (15–20%)
This domain covers Azure Storage and Azure Cosmos DB, which hold data that does not fit a table structure. It is the smallest domain, but the criteria for telling the services apart are clear-cut.
Key topics: Features of Azure Blob Storage, Azure Files, and Azure Table Storage; use cases and APIs for Azure Cosmos DB.
Exam tip: "Large unstructured files such as images, video, and backups" → Blob Storage. "A file share used by several servers" → Azure Files. "A globally distributed NoSQL database with fast responses" → Azure Cosmos DB.
Related posts: Non-relational Data Fundamentals, Azure Storage Deep Dive, Azure Cosmos DB Deep Dive
Domain 4: Describe an analytics workload on Azure (25–30%)
This domain covers the analytics flow, from ingesting and processing data to storing and visualizing it.
Key topics: Data ingestion and processing; analytical data stores; Azure Databricks and Microsoft Fabric; the difference between batch and streaming data; real-time analytics services; Power BI capabilities, data models, and choosing visualizations.
Exam tip: "Collect over an interval and process all at once" → batch. "Process continuously as events arrive" → streaming. For Power BI, organize what you know as a flow: connect to data, build a model, visualize, then share as reports and dashboards.
Related posts: Large-Scale Analytics Services, Data Ingestion Pipelines, Star Schema and Analytical Data Models, Real-Time Data Analytics, Fabric Real-Time Intelligence and Spark Structured Streaming, Power BI Essentials
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Where DP-900 Fits in the Azure Data Certification Path
DP-900 is the starting point for Azure data certifications; above it sit Associate certifications split by job role.
| Category | DP-900 | DP-300 | DP-700 | |------|------|------|------| | Level | Fundamentals | Associate | Associate | | Full Name | Azure Data Fundamentals | Azure Database Administrator Associate | Fabric Data Engineer Associate | | Main Audience | Any role, including non-technical | Administrators who run Azure SQL family databases | Engineers who build data pipelines with Microsoft Fabric | | Duration | 45 minutes | 100 minutes | 100 minutes | | Validity | Does not expire | 1 year (free online renewal) | 1 year (free online renewal) |
The relational versus non-relational distinction and the analytics flow you learn in DP-900 are assumed knowledge in the higher-level certifications. For database operations, continue with the DP-300 guide; if visualization and reporting are your goal, Power BI Data Analyst Associate (PL-300) is the next step. If your Azure fundamentals feel thin, read the AZ-900 guide alongside this one.
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Study Roadmap for Beginners
A step-by-step study order for people learning about data for the first time.
Step 1 (Week 1): Get the data concepts down. Work through structured, semi-structured, and unstructured data, and transactional versus analytical workloads, with an example for each.
Step 2 (Week 2): Learn relational data. Get comfortable with tables, keys, and normalization, and practice until you can read basic SQL statements. Put the differences among the three Azure SQL services in a table.
Step 3 (3–5 days): Sort out non-relational data. Separate Blob Storage, Azure Files, Table Storage, and Cosmos DB by "what kind of data goes in it?"
Step 4 (Weeks 3–4): Analytics workloads and practice questions. Place Databricks, Fabric, and Power BI on the ingest → process → store → visualize flow, then use the Practice Assessment on Microsoft Learn to find your weak domains.
The most common mistake is memorizing service names separately from what they are for. Practice asking "what shape is this data, and who reads it for what purpose?" before you pick a service, and most questions fall into place.
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Exam Day Checklist
You book the exam through Pearson VUE and choose between a test center and an online proctored session. Try the exam sandbox on Microsoft Learn before you book, so that on exam day you do not spend time figuring out the interface.