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Microsoft Exam Syllabus

DP-600 syllabus, skills measured, and exam topics

The DP-600 exam measures Maintain a data analytics solution, Prepare data, and Implement and manage semantic models. Use this page to review the current official syllabus, major domains, and source links before exam day.

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Skills measured by domain

Use the weighting table to decide where to spend the most study time.

Domain Weight
Maintain a data analytics solution 25–30%
Prepare data 45–50%
Implement and manage semantic models 25–30%

What to know before you study

These sections explain the role, audience, and exam framing behind the outline.

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
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  • Request accommodations: If you use assistive devices, require extra time, or need modification to any part of the exam experience, you can request an accommodation.
  • Take a free Practice Assessment: Test your skills with practice questions to help you prepare for the exam.

About 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. While 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.
  • 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.
  • Most questions cover features that are general availability (GA). The exam may contain questions on Preview features if those features are commonly used.

Audience profile

  • As a candidate for this exam, you should have subject matter expertise in designing, creating, and managing analytical assets, such as semantic models, warehouses, or lakehouses.
  • Your responsibilities for this role include:
  • Prepare and enrich data for analysis
  • Secure and maintain analytics assets
  • Implement and manage semantic models
  • You work closely with stakeholders for business requirements and partner with architects, analysts, engineers, and administrators.
  • You should also be able to query and analyze data by using Structured Query Language (SQL), Kusto Query Language (KQL), and Data Analysis Expressions (DAX).

Detailed outline

Scan each section as a working study checklist instead of one long wall of text.

Maintain a data analytics solution (25–30%)

  • Implement workspace-level access controls
  • Implement item-level access controls
  • Implement row-level, column-level, object-level, and file-level access control
  • Apply sensitivity labels to items
  • Endorse items
  • Configure version control for a workspace
  • Create and manage a Power BI Desktop project (.pbip)
  • Create and configure deployment pipelines
  • Perform impact analysis of downstream dependencies from lakehouses, warehouses, dataflows, and semantic models
  • Deploy and manage semantic models by using the XMLA endpoint
  • Create and update reusable assets, including Power BI template (.pbit) files, Power BI data source (.pbids) files, and shared semantic models

Prepare data (45–50%)

  • Create a data connection
  • Discover data by using OneLake catalog and Real-Time hub
  • Ingest or access data as needed
  • Choose between different data stores
  • Implement OneLake integration for Eventhouse and semantic models
  • Create views, functions, and stored procedures
  • Enrich data by adding new columns or tables
  • Implement a star schema for a lakehouse or warehouse
  • Denormalize data
  • Aggregate data
  • Merge or join data
  • Identify and resolve duplicate data, missing data, or null values

Implement and manage semantic models (25–30%)

  • Choose a storage mode
  • Implement a star schema for a semantic model
  • Implement relationships, such as bridge tables and many-to-many relationships
  • Write calculations that use DAX variables and functions, such as iterators, table filtering, windowing, and information functions
  • Implement calculation groups, dynamic format strings, and field parameters
  • Identify use cases for and configure large semantic model storage format
  • Design and build composite models
  • Implement performance improvements in queries and report visuals
  • Improve DAX performance
  • Configure Direct Lake, including default fallback and refresh behavior
  • Choose between Direct Lake on OneLake and Direct Lake on SQL endpoints
  • Implement incremental refresh for semantic models