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),

Skills measured

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