You are optimizing a dataflow in a Power BI Premium capacity. The dataflow performs multiple joins. You need to reduce the load time of the dataflow. Which two actions should you perform? Each…

Reducing memory assigned to dataflows would likely degrade performance, not improve it, especially for dataflows performing multiple joins which are memory intensive.

Executing non-foldable operations before foldable operations prevents query folding, forcing Power BI to process more data locally and increasing load times.

Executing foldable operations before non-foldable ones allows the Power Query engine to push transformations back to the source system, reducing the amount of data transferred and processed in Power BI. This concept, known as query folding, significantly improves performance by offloading work to more powerful source databases.

Placing all operations in a single dataflow can lead to complex, harder-to-manage dataflows and prevent the optimization benefits of separate staging and transformation layers.

Placing ingestion and transformation operations in separate dataflows (staging and transform dataflows) allows for better modularity, reusability of ingested data, and improved refresh performance. Staging dataflows can load data once, and multiple transformation dataflows can reference this data without re-ingesting it, reducing overall load on the source and Power BI capacity.