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Study guide for Exam DP-600: Implementing Analytics Solutions Using Microsoft Fabric

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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.

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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.

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 November 15, 2024

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, data 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).

Skills at a glance

  • Maintain a data analytics solution (25–30%)

  • Prepare data (45–50%)

  • Implement and manage semantic models (25–30%)

Maintain a data analytics solution (25–30%)

Implement security and governance

  • 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

Maintain the analytics development lifecycle

  • 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, data 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%)

Get data

  • Create a data connection

  • Discover data by using OneLake data hub and real-time hub

  • Ingest or access data as needed

  • Choose between a lakehouse, warehouse, or eventhouse

  • Implement OneLake integration for eventhouse and semantic models

Transform data

  • 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

  • Convert column data types

  • Filter data

Query and analyze data

  • Select, filter, and aggregate data by using the Visual Query Editor

  • Select, filter, and aggregate data by using SQL

  • Select, filter, and aggregate data by using KQL

Implement and manage semantic models (25–30%)

Design and build semantic models

  • 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

Optimize enterprise-scale semantic models

  • Implement performance improvements in queries and report visuals

  • Improve DAX performance

  • Configure Direct Lake, including default fallback and refresh behavior

  • Implement incremental refresh for semantic models

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.

Study resources Links to learning and documentation
Get trained Choose from self-paced learning paths and modules or take an instructor-led course
Find documentation Microsoft Fabric
What is a lakehouse?
What is data warehousing?
Data warehousing and analytics
Ask a question Microsoft Q&A | Microsoft Docs
Get community support Analytics on Azure - Microsoft Tech Community
Microsoft Fabric Blog
Follow Microsoft Learn Microsoft Learn - Microsoft Tech Community
Find a video Exam Readiness Zone
Data Exposed
Browse other Microsoft Learn shows

Change log

The table below summarizes the changes between the current and previous version of the skills measured. The functional groups are in bold typeface followed by the objectives within each group. The table is a comparison between the previous and current version of the exam skills measured and the third column describes the extent of the changes.

Skill area prior to November 15, 2024 Skill area as of November 15, 2024 Change
Audience profile Major
Plan, implement and manage a solution for data analytics Maintain a data analytics solution % of the exam increased
Plan a data analytics environment Deleted
Implement and manage a data analytics environment Implement security and governance Implement workspace Minor
Manage the analytics development lifecycle Maintain the analytics development lifecycle Minor
Prepare and serve data Prepare data % of the exam increased
Create objects in a lakehouse or warehouse Deleted
Copy data Get data Major
Transform data Transform data Major
Optimize performance Query and analyze data Major
Implement and manage semantic models Implement and manage semantic models % of the exam increased
Design and build semantic models Design and build semantic models Minor
Optimize enterprise-scale semantic models Optimize enterprise-scale semantic models Major
Explore and analyze data Deleted