1. Implement and manage a data analytics environment

           This section covers managing Power BI assets and identifying Azure data sources using Microsoft Purview, recommending Power BI admin portal settings, and suggesting monitoring and auditing solutions. It also includes integrating analytics platforms with existing IT infrastructure, such as identifying requirements, configuring Power BI capacity, and integrating Power BI with Azure Synapse Analytics and Azure Data Lake Storage Gen2. Managing the analytics development lifecycle involves committing code to source control, recommending deployment and source control strategies for Power BI assets, implementing deployment pipelines, and managing datasets using the XMLA endpoint. 

2. Query and transform data

         This module focuses on querying data using Azure Synapse Analytics, including identifying appropriate pools, recommending file types for serverless SQL pools, and querying relational data sources. It also covers ingesting and transforming data with Power BI, which includes identifying and improving data loading performance bottlenecks, creating scalable dataflows, managing data source privacy settings, and querying advanced data sources. 

3. Implement and manage data models

       This section details designing and building tabular models, including choosing between DirectQuery and external tools, creating calculation groups, writing DAX calculations, and designing large format and composite models. It also covers implementing enterprise-scale row-level and object-level security. Optimizing enterprise-scale data models involves identifying and implementing performance improvements, troubleshooting DAX performance, and optimizing models using tools like Tabular Editor 2 and VertiPaq Analyzer. 

4. Explore and visualize data

       This part of the course focuses on exploring data using Azure Synapse Analytics, including native visuals in Spark notebooks and the SQL results pane. Visualizing data with Power BI includes creating custom report themes, creating R or Python visuals, connecting to datasets using the XMLA endpoint, designing accessible reports, and configuring automatic page refresh. It also covers creating and distributing paginated reports.