When working with Power Query dataflows in Microsoft Power BI, optimizing performance and reusability is critical. An effective strategy is to separate data injection from data transformation. By…

Question

Options

• ASeparate data ingestion and data transformation into two distinct dataflows
• BAlways use on-premises data sources for faster data access
• COptimize data ingestion dataflows by filtering unnecessary data at the source
• DAvoid using analytical dataflows to simplify the architecture
• ELeverage query folding to improve data processing efficiency

• A
  74% (43)
• B
  9% (5)
• D
  17% (10)

Explanation

Three recommended best practices for improving performance and reusability in Power Query dataflows are: (A) Separating data ingestion from data transformation into distinct dataflows - this promotes reuse, simplifies debugging, and allows each layer to be refreshed independently; (C) Filtering unnecessary data at the source during ingestion - reducing data volume early minimizes memory usage and speeds up downstream transformations; and (E) Leveraging query folding - pushing transformation logic back to the source system allows the source to do the heavy lifting, significantly reducing the load on the Power Query engine. Option B is incorrect because on-premises sources add gateway latency and are not inherently faster. Option D is incorrect because analytical dataflows (those that output to a CDM-compliant store) are actually a recommended pattern for scalable architecture.

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