Data Mozart

DP-600 Certification

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DP-600 exam (Fabric Analytics Engineer) covers a broad range of skills and tools related to Microsoft Fabric. Think of components and concepts such as:

  • Data lakehouse
  • Data warehouse
  • Data modeling
  • Data transformation
  • Notebooks
  • Dataflows Gen2
  • Semantic model

I’ve created this page with the intention to keep all the learning resources in one single place. I hope that everyone who’s preparing to take this exam will find the page useful and that will in the end help you master all the necessary skills to become a certified Fabric Analytics Engineer.

Table of contents

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 *** Watch on YouTube
  • 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
  • Identify and resolve data loading performance bottlenecks in notebooks
  • Identify and resolve data loading performance bottlenecks in 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

Optimize enterprise-scale semantic models

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