Create reusable assets, including Power BI templates, Power BI data source (.pbids) files, and shared datasets
Identify an appropriate Azure Synapse pool when analyzing data
Recommend appropriate file types for querying serverless SQL pools
Query relational data sources in dedicated or serverless SQL pools, including querying partitioned data sources
Use a machine learning PREDICT function in a query
Identify data loading performance bottlenecks in Power Query or data sources
Implement performance improvements in Power Query and data sources
Create and manage scalable Power BI dataflows
Identify and manage privacy settings on data sources
Create queries, functions, and parameters by using the Power Query Advanced Editor
Query advanced data sources, including JSON, Parquet, APIs, and Azure Machine Learning models
Choose when to use DirectQuery for Power BI datasets
Choose when to use external tools, including DAX Studio and Tabular Editor 2
Create calculation groups
Write calculations that use DAX variables and functions, for example handling blanks or errors, creating virtual relationships, and working with iterators
Design and build a large format dataset