DP-600 Fabric Analytics Engineer Associate Practice Tests

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