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Microsoft Fabric – DP-600 Exam Preparation

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Preparation guide covering all areas of the DP-600 exam (Implementing Analytics Solutions in Microsoft Fabric)

This course covers every area of the DP-600 exam with 225+ questions (with answers and instruction). These areas include:

What you’ll learn

  • All areas of the DP-600 exam.
  • 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%).

Course Content

  • Introduction –> 4 lectures • 22min.
  • Plan, Implement and Manage a Solution (10-15%) –> 18 lectures • 1hr 48min.
  • Prepare and Serve Data (40-45%) –> 13 lectures • 1hr 35min.
  • Implement and manage semantic models (20–25%) –> 13 lectures • 1hr 36min.
  • Explore and analyze data (20–25%) –> 6 lectures • 48min.
  • Miscellaneous Questions –> 13 lectures • 23min.
  • Bonus Section –> 1 lecture • 1min.

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Requirements

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

  • 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
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