Microsoft Fabric Analytics Engineer (DP-600T00)

Module 1: Introduction to end-to-end analytics using Microsoft Fabric

• Describe end-to-end analytics in Microsoft Fabric

Module 2: Administer Microsoft Fabric

• Describe Fabric admin tasks
• Navigate the admin center
• Manage user access

Module 3 : Ingest Data with Dataflows Gen2 in Microsoft Fabric

• Describe Dataflow (Gen2) capabilities in Microsoft Fabric
• Create Dataflow (Gen2) solutions to ingest and transform data
• Include a Dataflow (Gen2) in a pipeline

Module 4 : Ingest data with Spark and Microsoft Fabric notebooks

• Ingest external data to Fabric lakehouses using Spark
• Configure external source authentication and optimization
• Load data into lakehouse as files or as Delta tables

Module 5 : Use Data Factory pipelines in Microsoft Fabric

• Describe pipeline capabilities in Microsoft Fabric
• Use the Copy Data activity in a pipeline
• Create pipelines based on predefined templates
• Run and monitor pipelines

Module 6 : Get started with lakehouses in Microsoft Fabric

• Describe core features and capabilities of lakehouses in Microsoft Fabric
• Create a lakehouse
• Ingest data into files and tables in a lakehouse
• Query lakehouse tables with SQL

Module 7 : Organize a Fabric lakehouse using medallion architecture design

• Describe the principles of using the medallion architecture in data management.
• Apply the medallion architecture framework within the Microsoft Fabric environment.
• Analyze data stored in the lakehouse using DirectLake in Power BI.
• Describe best practices for ensuring the security and governance of data stored in the medallion architecture.

Module 8 : Use Apache Spark in Microsoft Fabric

• Configure Spark in a Microsoft Fabric workspace
• Identify suitable scenarios for Spark notebooks and Spark jobs
• Use Spark dataframes to analyze and transform data
• Use Spark SQL to query data in tables and views
• Visualize data in a Spark notebook

Module 9 : Work with Delta Lake tables in Microsoft Fabric

• Understand Delta Lake and delta tables in Microsoft Fabric
• Create and manage delta tables using Spark
• Use Spark to query and transform data in delta tables
• Use delta tables with Spark structured streaming

Module 10 : Get started with data warehouses in Microsoft Fabric

• Describe data warehouses in Fabric
• Understand a data warehouse vs a data Lakehouse
• Work with data warehouses in Fabric
• Create and manage datasets within a data warehouse

Module 11 : Load data into a Microsoft Fabric data warehouse

• Learn different strategies to load data into a data warehouse in Microsoft Fabric.
• Learn how to build a data pipeline to load a warehouse in Microsoft Fabric.
• Learn how to load data in a warehouse using T-SQL.
• Learn how to load and transform data with dataflow (Gen 2).

Module 12 : Query a data warehouse in Microsoft Fabric

• Use SQL query editor to query a data warehouse.
• Explore how visual query editor works.
• Learn how to connect and query a data warehouse using SQL Server Management Studio.

Module 13 : Monitor a Microsoft Fabric data warehouse

• Monitor capacity unit usage with the Microsoft Fabric Capacity Metrics app.
• Monitor current activity in the data warehouse with dynamic management views.
• Monitor querying trends with query insights views.

Module 14 : Understand scalability in Power BI

• Describe the importance of building scalable data models
• Implement Power BI data modeling best practices
• Use the Power BI large dataset storage format

Module 15 : Create Power BI model relationships

• Understand how model relationship work.
• Set up relationships.
• Use DAX relationship functions.
• Understand relationship evaluation.

Module 16 : Use tools to optimize Power BI performance

• Optimize queries using performance analyzer.
• Troubleshoot DAX performance using DAX Studio.
• Optimize a data model using Tabular Editor.

Module 17 : Enforce Power BI model security

• Restrict access to Power BI model data with RLS.
• Restrict access to Power BI model objects with OLS.
• Apply good development practices to enforce Power BI model security.