Microsoft DP-900 is a current beginner-level Azure Data Fundamentals exam, with skills updated July 21, 2026. It asks candidates to understand the distinctions between relational and non-relational data, data representations and transaction versus analytics workloads, then relate those concepts to Azure storage and processing products. The Microsoft DP-900 exam is not a prerequisite for advanced credentials, but it gives a useful vocabulary for understanding data platform responsibilities. The current issuer guide, not an old percentages table, should define the precise objective emphasis.
Prerequisites for the Azure Data Fundamentals Certification
While the Azure Data Fundamentals (DP-900) certification is designed for individuals starting their journey in cloud data management, having a foundational understanding of certain concepts can greatly enhance your preparation and chances of success. Though these prerequisites are not strict requirements, acquiring some basic knowledge of the following areas will provide you with a significant advantage as you prepare for the exam.
A Basic Understanding of Relational and Non-Relational Data Before diving into the DP-900 exam, it’s beneficial to have a solid grasp of the difference between relational and non-relational data. Relational data is typically stored in tables with rows and columns, adhering to a structured format, whereas non-relational data (often referred to as NoSQL) includes various types of unstructured data such as JSON files, key-value pairs, or graph databases.
Being familiar with these two data models will help you understand the types of data you will work with in the Azure ecosystem. Azure offers a wide range of services for both types of data, including relational databases like Azure SQL Database and non-relational options such as Azure Cosmos DB. Having this understanding will allow you to grasp the foundational services in Azure that are designed to handle both structured and unstructured data effectively.
Familiarity with Data Manipulation Language (DML) and Data Definition Language (DDL) Another important area to familiarize yourself with is Data Manipulation Language (DML) and Data Definition Language (DDL). DML refers to the SQL commands used to manage data within a database, including SELECT, INSERT, UPDATE, and DELETE operations. These commands are crucial for querying and altering data stored in databases.
On the other hand, DDL deals with the structure of databases, allowing users to define, modify, or remove tables, schemas, and other database objects. Common DDL commands include CREATE, ALTER, DROP, and TRUNCATE. A basic understanding of these languages is helpful because much of the exam content focuses on working with databases, queries, and data manipulation. Familiarity with these concepts will enable you to better understand how data is managed and structured in Azure’s cloud services.
Basic Conceptual Knowledge of Queries, Schemas, and Views
A conceptual understanding of queries, schemas, and views will significantly aid in your preparation. A query is a request for information from a database, typically written in SQL. Understanding how to structure and write queries to retrieve and manipulate data is essential for anyone working with databases.
A schema is essentially the blueprint of a database, outlining how data is organized and the relationships between different tables. Knowing how schemas work will help you understand how data is stored and accessed within Azure’s database services.
Views are virtual tables that allow users to query data without modifying the underlying tables. They provide a way to simplify complex queries and enhance data security by allowing users to access only specific columns or rows of data. Familiarity with these concepts will give you a deeper insight into how Azure’s data management systems are structured and how you can interact with them.
Awareness of Azure Managed Services and Storage Containers As the Azure Data Fundamentals certification focuses on the Azure cloud platform, having an awareness of Azure’s managed services and storage containers is critical. Azure offers a range of managed services, meaning Microsoft handles the underlying infrastructure and maintenance, allowing you to focus on managing and processing your data.
Some of the key managed services in Azure that you may encounter include Azure SQL Database, Azure Cosmos DB, and Azure Data Lake Storage. These services take care of tasks such as scaling, backups, and high availability, which can be challenging in a traditional on-premises environment.
In addition, you should have a basic understanding of storage containers in Azure. Containers like Azure Blob Storage and Azure Data Lake Storage are used to store large amounts of unstructured data, such as images, videos, and logs. Familiarity with these storage solutions will help you better understand how to store and retrieve data efficiently in the cloud.
Why These Prerequisites Matter While the Azure Data Fundamentals certification is designed for those at the beginning of their cloud data journey, having these fundamental concepts in place will provide you with a clearer and more structured understanding of the content covered in the exam. These prerequisites ensure that you can engage with the study materials more effectively and have a practical understanding of the concepts when taking the exam.
In particular, understanding the basics of databases, data types, and the cloud environment will help you comprehend the broader topics covered in the certification exam, such as Azure data services, data security, and data analytics. Although these prerequisites are not mandatory, they will undoubtedly enhance your learning experience and increase your chances of passing the exam on your first attempt.
Core Topics Covered in the Azure Data Fundamentals DP-900 Exam
To succeed in the Azure Data Fundamentals (DP-900) exam, it’s essential to understand the key areas of the exam syllabus. The topics are divided into distinct domains, each covering various aspects of data management and Azure services. By focusing on these areas and mastering the underlying concepts, you’ll be well-prepared for the exam. The exam covers a broad range of subjects, from core data concepts to advanced analytics workloads, ensuring that candidates are equipped with a comprehensive understanding of Azure’s data capabilities.
Below are the core topics covered in the DP-900 exam, broken down by domain and their corresponding weight in the overall exam:
Types of Core Data Workloads The core data workloads refer to the different categories of data that organizations handle, which can be broadly classified into structured, semi-structured, and unstructured data. Structured data refers to data that is highly organized and typically stored in relational databases, such as tables with rows and columns. Semi-structured data, like JSON or XML, lacks a rigid structure but still includes tags or markers that make it easier to process. Unstructured data includes formats like text, images, and videos, which don’t fit neatly into tables.
Candidates should be able to differentiate between these data types and understand the unique challenges and opportunities associated with each. This knowledge will be important when navigating Azure’s data services, as each data type is managed differently within the cloud environment.
Key Data Analytics Concepts Another critical aspect of this domain is data analytics. Azure offers powerful tools for processing and analyzing large datasets to extract meaningful insights. You should be familiar with key data analytics concepts, such as data transformation, data aggregation, and data exploration. Additionally, understanding the role of analytics in decision-making and business intelligence will be key to working with Azure’s analytics services.
Understanding Relational Data Workloads
Relational data is stored in tables with defined relationships between different entities. Understanding the relational data workloads is crucial, as this will help you understand how data is stored, accessed, and managed in systems like Azure SQL Database and Azure Database for MySQL.
You will need to grasp how relational databases organize data using structured formats, enabling complex queries and transactions. This includes understanding primary and foreign keys, normalization, and indexing. Additionally, you should know how relational databases are used in various industries for transactional systems, reporting, and analytics.
Azure Relational Data Services Microsoft Azure provides several managed relational data services, including Azure SQL Database, Azure SQL Managed Instance, and Azure Database for MySQL. These services are designed to help organizations easily deploy, manage, and scale relational databases in the cloud. It’s important to familiarize yourself with the features, use cases, and capabilities of these services, as they are central to the Azure ecosystem.
Basic Management of Relational Data This subdomain focuses on the fundamental tasks involved in managing relational data within Azure. Key areas include provisioning and configuring relational databases, performing backups, ensuring high availability, and applying security measures to protect sensitive data. You should understand how to manage performance and scalability within Azure’s relational data offerings.
SQL Query Techniques
Another critical area is SQL query techniques. Structured Query Language (SQL) is the standard language used to interact with relational databases. You will need to understand how to write and optimize basic SQL queries for retrieving, updating, inserting, and deleting data. Basic query structures, filtering data with WHERE clauses, joining tables, and performing aggregate functions are essential skills to master.
Non-Relational Data Workloads Overview Non-relational data workloads include data types such as JSON, XML, graph data, and key-value pairs. These data types are used in scenarios where a relational database may not be the best fit, such as when handling large amounts of unstructured data or when scalability and flexibility are a priority.
Azure provides various services to store and process non-relational data, and you should understand how these workloads differ from relational data. It’s also important to be aware of use cases where non-relational databases excel, such as real-time data processing, IoT data, and content management systems.
Azure Non-Relational Data Services Azure offers a variety of non-relational data services to meet the diverse needs of modern data applications. Key services to focus on include Azure Cosmos DB, which supports multiple non-relational data models (including document, key-value, and graph databases), and Azure Blob Storage, which is ideal for storing large amounts of unstructured data like media files.
You should also be familiar with how Azure’s NoSQL solutions differ from traditional relational databases in terms of scalability, consistency, and flexibility. Understanding the architecture and use cases of these services will be crucial for working with non-relational data in Azure.
Basic Management Tasks for Non-Relational Data
Managing non-relational data in Azure involves tasks such as data ingestion, partitioning, consistency management, and ensuring performance at scale. You will need to know how to configure and manage services like Azure Cosmos DB and Azure Blob Storage, including monitoring and optimizing performance to handle high-volume, low-latency data access.
Overview of Analytics Workloads This subdomain introduces the key analytics workloads in Azure, which include data warehousing, data transformation, and big data analytics. You should be familiar with how Azure enables organizations to process and analyze massive datasets, and the different services that cater to various analytics needs.
Components of Modern Data Warehouses Data warehousing plays a central role in analytics workloads. Understanding the components of a modern data warehouse and how Azure provides solutions for storing and querying large volumes of structured data will be essential. Azure’s Azure Synapse Analytics is a key service in this area, providing a unified experience for data warehousing, big data analytics, and machine learning.
You should understand how to set up and manage data warehouses, and how Azure’s data services integrate to create a comprehensive data ecosystem.
Data Ingestion and Processing in Azure
Data ingestion involves bringing data from various sources into Azure, where it can be processed and analyzed. Key services in this area include Azure Data Factory (for orchestrating data pipelines), Azure Stream Analytics (for real-time data processing), and Azure Databricks (for big data analytics). Familiarize yourself with the workflows and techniques used to ingest and process data within Azure.
Data Visualization with Microsoft Power BI Once data is processed and analyzed, data visualization is crucial for presenting the findings in an easily digestible format. Microsoft Power BI is the leading tool for creating interactive reports and dashboards. Understanding how to integrate Power BI with Azure’s data services and how to visualize data effectively will be key in preparing for this section of the exam.
Mastering Core Topics for Exam Success The DP-900 exam covers a broad spectrum of topics within Azure’s data services, from core concepts to advanced analytics. By thoroughly studying the above domains, candidates can develop a well-rounded understanding of how to work with relational and non-relational data, manage data securely, and leverage Azure’s powerful analytics tools. This knowledge is essential not only for passing the exam but also for establishing a solid foundation in cloud data management using Azure.
How to Prepare for the Microsoft Azure DP-900 Exam
The Microsoft Azure DP-900 exam is designed to assess your ability to understand and implement data management solutions on the Azure platform. Successfully passing this exam requires not only a solid understanding of the concepts but also practical knowledge of how to apply them using Azure’s data services. Proper preparation is key to achieving success, and a well-structured study plan can make a significant difference in your exam readiness.
Follow the steps below for a comprehensive and strategic approach to preparing for the DP-900 exam:
Step 1: Leverage Microsoft’s Official Learning Path for DP-900 Microsoft provides a structured learning path tailored specifically to the DP-900 exam. This learning path is an excellent resource that covers all the topics you need to know and breaks them down into digestible modules. The learning path includes:
- Core Data Concepts (5 modules): This section covers the foundational principles of data management, including relational and non-relational data, cloud concepts, and analytics fundamentals.
- Relational Data in Azure (3 modules): Focuses on how relational data is stored, queried, and managed within Azure, including an introduction to Azure SQL Database and other relational services.
- Non-Relational Data in Azure (3 modules): Covers Azure’s non-relational services such as Azure Cosmos DB and Azure Blob Storage, and provides insights into how non-relational data is handled in the cloud.
- Modern Data Warehouse Analytics (4 modules): Delves into the Azure Synapse Analytics platform and other tools for processing, storing, and analyzing large datasets.
You can access the official DP-900 learning path directly on Microsoft’s website. Going through the entire path will ensure that you grasp all the necessary concepts and familiarize yourself with Azure’s data services.
Step 2: Dive into Microsoft’s Official Documentation While the official learning path is a great starting point, Microsoft documentation is where you’ll find the most comprehensive and detailed explanations of Azure data services. After completing the learning path, revisit the documentation to deepen your understanding of specific topics.
The official documentation offers:
- In-depth explanations of Azure services such as Azure SQL Database, Azure Cosmos DB, Azure Data Factory, and more.
- Practical examples and use cases to show you how these services are applied in real-world scenarios.
- Best practices for working with Azure’s data management tools, ensuring that you not only pass the exam but are also prepared to use these tools effectively in a professional environment.
Reading through the documentation will give you a deeper insight into how Azure’s data tools function, providing valuable context for your exam preparation.
Compare three small workloads: transactional records in a relational service, flexible documents in a non-relational store, and a dataset processed for analytics. Explain the model and operations each use case requires.