Description
What you’ll learn
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All areas of the DP-600 exam
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Plan, implement, and manage a solution for data analytics (10–15%)
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Prepare and serve data (40–45%)
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Implement and manage semantic models (20–25%)
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Explore and analyze data (20–25%)
This course covers every area of the DP-600 exam with 225+ questions (with answers and instruction). These areas include:
Plan, implement, and manage a solution for data analytics (10–15%)
Plan a data analytics environment
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Identify requirements for a solution, including components, features, performance, and capacity stock-keeping units (SKUs)
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Recommend settings in the Fabric admin portal
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Choose a data gateway type
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Create a custom Power BI report theme
Implement and manage a data analytics environment
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Implement workspace and item-level access controls for Fabric items
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Implement data sharing for workspaces, warehouses, and lakehouses
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Manage sensitivity labels in semantic models and lakehouses
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Configure Fabric-enabled workspace settings
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Manage Fabric capacity
Manage the analytics development lifecycle
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Implement version control for a workspace
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Create and manage a Power BI Desktop project (.pbip)
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Plan and implement deployment solutions
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Perform impact analysis of downstream dependencies from lakehouses, data warehouses, dataflows, and semantic models
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Deploy and manage semantic models by using the XMLA endpoint
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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
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Ingest data by using a data pipeline, dataflow, or notebook
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Create and manage shortcuts
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Implement file partitioning for analytics workloads in a lakehouse
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Create views, functions, and stored procedures
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Enrich data by adding new columns or tables
Copy data
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Choose an appropriate method for copying data from a Fabric data source to a lakehouse or warehouse
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Copy data by using a data pipeline, dataflow, or notebook
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Add stored procedures, notebooks, and dataflows to a data pipeline
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Schedule data pipelines
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Schedule dataflows and notebooks
Transform data
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Implement a data cleansing process
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Implement a star schema for a lakehouse or warehouse, including Type 1 and Type 2 slowly changing dimensions
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Implement bridge tables for a lakehouse or a warehouse
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Denormalize data
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Aggregate or de-aggregate data
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Merge or join data
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Identify and resolve duplicate data, missing data, or null values
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Convert data types by using SQL or PySpark
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Filter data
Optimize performance
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Identify and resolve data loading performance bottlenecks in dataflows, notebooks, and SQL queries
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Implement performance improvements in dataflows, notebooks, and SQL queries
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Identify and resolve issues with Delta table file sizes
Implement and manage semantic models (20–25%)
Design and build semantic models
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Choose a storage mode, including Direct Lake
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Identify use cases for DAX Studio and Tabular Editor 2
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Implement a star schema for a semantic model
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Implement relationships, such as bridge tables and many-to-many relationships
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Write calculations that use DAX variables and functions, such as iterators, table filtering, windowing, and information functions
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Implement calculation groups, dynamic strings, and field parameters
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Design and build a large format dataset
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Design and build composite models that include aggregations
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Implement dynamic row-level security and object-level security
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Validate row-level security and object-level security
Optimize enterprise-scale semantic models
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Implement performance improvements in queries and report visuals
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Improve DAX performance by using DAX Studio
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Optimize a semantic model by using Tabular Editor 2
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Implement incremental refresh
Explore and analyze data (20–25%)
Perform exploratory analytics
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Implement descriptive and diagnostic analytics
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Integrate prescriptive and predictive analytics into a visual or report
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Profile data
Query data by using SQL
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Query a lakehouse in Fabric by using SQL queries or the visual query editor
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Query a warehouse in Fabric by using SQL queries or the visual query editor
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Connect to and query datasets by using the XMLA endpoint
Who this course is for:
- Anyone preparing to take and pass the DP-600 exam
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