DP-600: Microsoft Fabric Analytics Engineer

• 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

   

4 days