Study guide for Exam DP-600: Implementing Analytics Solutions Using Microsoft Fabric
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 July 22, 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 July 22, 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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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 July 22, 2024
Audience profile
As a candidate for this exam, you should have subject matter expertise in designing, creating, and deploying enterprise-scale data analytics solutions.
Your responsibilities for this role include transforming data into reusable analytics assets by using Microsoft Fabric components, such as:
Lakehouses
Data warehouses
Notebooks
Dataflows
Data pipelines
Semantic models
Reports
You implement analytics best practices in Fabric, including version control and deployment.
To implement solutions as a Fabric analytics engineer, you partner with other roles, such as:
Solution architects
Data engineers
Data scientists
AI engineers
Database administrators
Power BI data analysts
In addition to in-depth work with the Fabric platform, you need experience with:
Data modeling
Data transformation
Git-based source control
Exploratory analytics
Programming languages (including Structured Query Language (SQL), Data Analysis Expressions (DAX), and PySpark)
Skills at a glance
Plan, implement, and manage a solution for data analytics (10–15%)
Prepare and serve data (40–45%)
Implement and manage semantic models (20–25%)
Explore and analyze data (20–25%)
Plan, implement, and manage a solution for data analytics (10–15%)
Plan a data analytics environment
Identify requirements for a solution, including components, features, performance, and capacity stock-keeping units (SKUs)
Recommend settings in the Fabric admin portal
Choose a data gateway type
Create a custom Power BI report theme
Implement and manage a data analytics environment
Implement workspace and item-level access controls for Fabric items
Implement data sharing for workspaces, warehouses, and lakehouses
Manage sensitivity labels in semantic models and lakehouses
Configure Fabric-enabled workspace settings
Manage Fabric capacity and configure capacity settings
Manage the analytics development lifecycle
Implement version control for a workspace
Create and manage a Power BI Desktop project (.pbip)
Plan and implement deployment solutions
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 and serve data (40–45%)
Create objects in a lakehouse or warehouse
Ingest data by using a data pipeline, dataflow, or notebook
Create and manage shortcuts
Implement file partitioning for analytics workloads in a lakehouse
Create views, functions, and stored procedures
Enrich data by adding new columns or tables
Copy data
Choose an appropriate method for copying data from a Fabric data source to a lakehouse or warehouse
Copy data by using a data pipeline, dataflow, or notebook
Implement Fast Copy when using dataflows
Add stored procedures, notebooks, and dataflows to a data pipeline
Schedule data pipelines
Schedule dataflows and notebooks
Transform data
Implement a data cleansing process
Implement a star schema for a lakehouse or warehouse, including Type 1 and Type 2 slowly changing dimensions
Implement bridge tables for a lakehouse or a warehouse
Denormalize data
Aggregate or de-aggregate data
Merge or join data
Identify and resolve duplicate data, missing data, or null values
Convert data types by using SQL or PySpark
Filter data
Optimize performance
Identify and resolve data loading performance bottlenecks in dataflows, notebooks, and SQL queries
Implement performance improvements in dataflows, notebooks, and SQL queries
Identify and resolve issues with the structure or size of Delta table files (including v-order and optimized writes)
Implement and manage semantic models (20–25%)
Design and build semantic models
Choose a storage mode, including Direct Lake
Identify use cases for DAX Studio and Tabular Editor 2
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 strings, and field parameters
Design and build a large format dataset
Design and build composite models that include aggregations
Implement dynamic row-level security and object-level security
Validate row-level security and object-level security
Optimize enterprise-scale semantic models
Implement performance improvements in queries and report visuals
Improve DAX performance by using DAX Studio
Optimize a semantic model by using Tabular Editor 2
Implement incremental refresh
Explore and analyze data (20–25%)
Perform exploratory analytics
Implement descriptive and diagnostic analytics
Integrate prescriptive and predictive analytics into a visual or report
Profile data
Query data by using SQL
Query a lakehouse in Fabric by using SQL queries or the visual query editor
Query a warehouse in Fabric by using SQL queries or the visual query editor
Connect to and query datasets by using the XMLA endpoint
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
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 July 22, 2024 | Skill area as of July 22, 2024 | Change |
|---|---|---|
| Audience profile | Minor | |
| Plan, implement and manage a solution for data analytics | Plan, implement and manage a solution for data analytics | No change |
| Plan a data analytics environment | Plan a data analytics environment | No change |
| Implement and manage a data analytics environment | Implement and manage a data analytics environment | Minor |
| Manage the analytics development lifecycle | Manage the analytics development lifecycle | No change |
| Prepare and serve data | Prepare and serve data | No change |
| Create objects in a lakehouse or warehouse | Create objects in a lakehouse or warehouse | No change |
| Copy data | Copy data | Minor |
| Transform data | Transform data | No change |
| Optimize performance | Optimize performance | Minor |
| Implement and manage semantic models | Implement and manage semantic models | No change |
| Design and build semantic models | Design and build semantic models | No change |
| Optimize enterprise-scale semantic models | Optimize enterprise-scale semantic models | No change |
| Explore and analyze data | Explore and analyze data | No change |
| Perform exploratory analytics | Perform exploratory analytics | No change |
| Query data by using SQL | Query data by using SQL | No change |
Skills measured prior to July 22, 2024
Audience profile
As a candidate for this exam, you should have subject matter expertise in designing, creating, and deploying enterprise-scale data analytics solutions.
Your responsibilities for this role include transforming data into reusable analytics assets by using Microsoft Fabric components, such as:
Lakehouses
Data warehouses
Notebooks
Dataflows
Data pipelines
Semantic models
Reports
You implement analytics best practices in Fabric, including version control and deployment.
To implement solutions as a Fabric analytics engineer, you partner with other roles, such as:
Solution architects
Data engineers
Data scientists
AI engineers
Database administrators
Power BI data analysts
In addition to in-depth work with the Fabric platform, you need experience with:
Data modeling
Data transformation
Git-based source control
Exploratory analytics
Languages, including Structured Query Language (SQL), Data Analysis Expressions (DAX), and PySpark
Skills at a glance
Plan, implement, and manage a solution for data analytics (10–15%)
Prepare and serve data (40–45%)
Implement and manage semantic models (20–25%)
Explore and analyze data (20–25%)
Plan, implement, and manage a solution for data analytics (10–15%)
Plan a data analytics environment
Identify requirements for a solution, including components, features, performance, and capacity stock-keeping units (SKUs)
Recommend settings in the Fabric admin portal
Choose a data gateway type
Create a custom Power BI report theme
Implement and manage a data analytics environment
Implement workspace and item-level access controls for Fabric items
Implement data sharing for workspaces, warehouses, and lakehouses
Manage sensitivity labels in semantic models and lakehouses
Configure Fabric-enabled workspace settings
Manage Fabric capacity
Manage the analytics development lifecycle
Implement version control for a workspace
Create and manage a Power BI Desktop project (.pbip)
Plan and implement deployment solutions
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 and serve data (40–45%)
Create objects in a lakehouse or warehouse
Ingest data by using a data pipeline, dataflow, or notebook
Create and manage shortcuts
Implement file partitioning for analytics workloads in a lakehouse
Create views, functions, and stored procedures
Enrich data by adding new columns or tables
Copy data
Choose an appropriate method for copying data from a Fabric data source to a lakehouse or warehouse
Copy data by using a data pipeline, dataflow, or notebook
Add stored procedures, notebooks, and dataflows to a data pipeline
Schedule data pipelines
Schedule dataflows and notebooks
Transform data
Implement a data cleansing process
Implement a star schema for a lakehouse or warehouse, including Type 1 and Type 2 slowly changing dimensions
Implement bridge tables for a lakehouse or a warehouse
Denormalize data
Aggregate or de-aggregate data
Merge or join data
Identify and resolve duplicate data, missing data, or null values
Convert data types by using SQL or PySpark
Filter data
Optimize performance
Identify and resolve data loading performance bottlenecks in dataflows, notebooks, and SQL queries
Implement performance improvements in dataflows, notebooks, and SQL queries
Identify and resolve issues with Delta table file sizes
Implement and manage semantic models (20–25%)
Design and build semantic models
Choose a storage mode, including Direct Lake
Identify use cases for DAX Studio and Tabular Editor 2
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 strings, and field parameters
Design and build a large format dataset
Design and build composite models that include aggregations
Implement dynamic row-level security and object-level security
Validate row-level security and object-level security
Optimize enterprise-scale semantic models
Implement performance improvements in queries and report visuals
Improve DAX performance by using DAX Studio
Optimize a semantic model by using Tabular Editor 2
Implement incremental refresh
Explore and analyze data (20–25%)
Perform exploratory analytics
Implement descriptive and diagnostic analytics
Integrate prescriptive and predictive analytics into a visual or report
Profile data
Query data by using SQL
Query a lakehouse in Fabric by using SQL queries or the visual query editor
Query a warehouse in Fabric by using SQL queries or the visual query editor
Connect to and query datasets by using the XMLA endpoint