Study guide for Exam DP-900: Microsoft Azure Data Fundamentals
Purpose of this document
This study guide should help you understand what to expect on the exam and includes a summary of the topics the exam might cover and links to additional resources. The information and materials in this document should help you focus your studies as you prepare for the exam.
| Useful links | Description |
|---|---|
| Review the skills measured as of February 1, 2024 | This list represents the skills measured AFTER the date provided. Study this list if you plan to take the exam AFTER that date. |
| Review the skills measured prior to February 1, 2024 | Study this list of skills if you take your exam PRIOR to the date provided. |
| Change log | You can go directly to the change log if you want to see the changes that will be made on the date provided. |
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| Exam scoring and score reports | A score of 700 or greater is required to pass. |
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| Take a free Practice Assessment | Test your skills with practice questions to help you prepare for the exam. |
Updates to the exam
Our exams are updated periodically to reflect skills that are required to perform a role. We have included two versions of the Skills Measured objectives depending on when you are taking the exam.
We always update the English language version of the exam first. Some exams are localized into other languages, and those are updated approximately eight weeks after the English version is updated. Although Microsoft makes every effort to update localized versions as noted, there may be times when the localized versions of an exam are not updated on this schedule. Other available languages are listed in the Schedule Exam section of the Exam Details webpage. If the exam isn't available in your preferred language, you can request an additional 30 minutes to complete the exam.
Note
The bullets that follow each of the skills measured are intended to illustrate how we are assessing that skill. Related topics may be covered in the exam.
Note
Most questions cover features that are general availability (GA). The exam may contain questions on Preview features if those features are commonly used.
Skills measured as of February 1, 2024
Audience profile
This exam is an opportunity to demonstrate your knowledge of core data concepts and related Microsoft Azure data services. As a candidate for this exam, you should have familiarity with Exam DP-900’s self-paced or instructor-led learning material.
This exam is intended for you, if you’re a candidate beginning to work with data in the cloud.
You should be familiar with:
The concepts of relational and non-relational data.
Different types of data workloads such as transactional or analytical.
You can use Azure Data Fundamentals to prepare for other Azure role-based certifications like Azure Database Administrator Associate or Azure Data Engineer Associate, but it is not a prerequisite for any of them.
Skills at a glance
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%)
Describe core data concepts (25–30%)
Describe ways to represent data
Describe features of structured data
Describe features of semi-structured
Describe features of unstructured data
Identify options for data storage
Describe common formats for data files
Describe types of databases
Describe common data workloads
Describe features of transactional workloads
Describe features of analytical workloads
Identify roles and responsibilities for data workloads
Describe responsibilities for database administrators
Describe responsibilities for data engineers
Describe responsibilities for data analysts
Identify considerations for relational data on Azure (20–25%)
Describe relational concepts
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
Describe the Azure SQL family of products including Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure Virtual Machines
Identify Azure database services for open-source database systems
Describe considerations for working with non-relational data on Azure (15–20%)
Describe capabilities of Azure storage
Describe Azure Blob storage
Describe Azure File storage
Describe Azure Table storage
Describe capabilities and features of Azure Cosmos DB
Identify use cases for Azure Cosmos DB
Describe Azure Cosmos DB APIs
Describe an analytics workload on Azure (25–30%)
Describe common elements of large-scale analytics
Describe considerations for data ingestion and processing
Describe options for analytical data stores
Describe Azure services for data warehousing, including Azure Synapse Analytics, Azure Databricks, Microsoft Fabric, Azure HDInsight, and Azure Data Factory
Describe consideration for real-time data analytics
Describe the difference between batch and streaming data
Identify Microsoft cloud services for real-time analytics
Describe data visualization in Microsoft Power BI
Identify capabilities of Power BI
Describe features of data models in Power BI
Identify appropriate visualizations for data
Study resources
We recommend that you train and get hands-on experience before you take the exam. We offer self-study options and classroom training as well as links to documentation, community sites, and videos.
Change log
Key to understanding the table: The topic groups (also known as functional groups) are in bold typeface followed by the objectives within each group. The table is a comparison between the two versions of the exam skills measured and the third column describes the extent of the changes.
| Skill area prior to February 1, 2024 | Skill area as of February 1, 2024 | Change |
|---|---|---|
| Audience profile | No change | |
| Describe core data concepts | Describe core data concepts | No change |
| Describe ways to represent data | Describe ways to represent data | No change |
| Identify options for data storage | Identify options for data storage | No change |
| Describe common data workloads | Describe common data workloads | No change |
| Identify roles and responsibilities for data workloads | Identify roles and responsibilities for data workloads | No change |
| Identify considerations for relational data on Azure | Identify considerations for relational data on Azure | No change |
| Describe relational concepts | Describe relational concepts | No change |
| Describe relational Azure data services | Describe relational Azure data services | Minor |
| Describe considerations for working with non-relational data on Azure | Describe considerations for working with non-relational data on Azure | No change |
| Describe capabilities of Azure storage | Describe capabilities of Azure storage | No change |
| Describe capabilities and features of Azure Cosmos DB | Describe capabilities and features of Azure Cosmos DB | No change |
| Describe an analytics workload on Azure | Describe an analytics workload on Azure | No change |
| Describe common elements of large-scale analytics | Describe common elements of large-scale analytics | Minor |
| Describe consideration for real-time data analytics | Describe consideration for real-time data analytics | Minor |
| Describe data visualization in Microsoft Power BI | Describe data visualization in Microsoft Power BI | No change |
Skills measured prior to February 1, 2024
Audience profile
This exam is an opportunity to demonstrate knowledge of core data concepts and related Microsoft Azure data services. Candidates for this exam should have familiarity with DP-900’s self-paced or instructor-led learning material.
This exam is intended for candidates beginning to work with data in the cloud.
Candidates should be familiar with the concepts of relational and non-relational data, and different types of data workloads such as transactional or analytical.
Azure Data Fundamentals can be used to prepare for other Azure role-based certifications like Azure Database Administrator Associate or Azure Data Engineer Associate, but it is not a prerequisite for any of them.
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%)
Describe core data concepts (25–30%)
Describe ways to represent data
Describe features of structured data
Describe features of semi-structured
Describe features of unstructured data
Identify options for data storage
Describe common formats for data files
Describe types of databases
Describe common data workloads
Describe features of transactional workloads
Describe features of analytical workloads
Identify roles and responsibilities for data workloads
Describe responsibilities for database administrators
Describe responsibilities for data engineers
Describe responsibilities for data analysts
Identify considerations for relational data on Azure (20–25%)
Describe relational concepts
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
Describe the Azure SQL family of products including Azure SQL Database, Azure SQL
Managed Instance, and SQL Server on Azure Virtual Machines
Identify Azure database services for open-source database systems
Describe considerations for working with non-relational data on Azure (15–20%)
Describe capabilities of Azure storage
Describe Azure Blob storage
Describe Azure File storage
Describe Azure Table storage
Describe capabilities and features of Azure Cosmos DB
Identify use cases for Azure Cosmos DB
Describe Azure Cosmos DB APIs
Describe an analytics workload on Azure (25–30%)
Describe common elements of large-scale analytics
Describe considerations for data ingestion and processing
Describe options for analytical data stores
Describe Azure services for data warehousing, including Azure Synapse Analytics, Azure Databricks, Azure HDInsight, and Azure Data Factory
Describe consideration for real-time data analytics
Describe the difference between batch and streaming data
Describe technologies for real-time analytics including Azure Stream Analytics, Azure Synapse Data Explorer, and Spark Structured Streaming
Describe data visualization in Microsoft Power BI
Identify capabilities of Power BI
Describe features of data models in Power BI
Identify appropriate visualizations for data