A Power BI Desktop model currently imports 120 million detailed transaction rows from Azure SQL Database. Report visuals summarize revenue at day level and never expose individual transactions. To decrease in-memory model size and accelerate DAX queries while keeping calculation results unchanged, which action should you take before the data is loaded into the model?
Change the table's storage mode from Import to DirectQuery.
Enable column profiling and column quality options in Power Query.
Use Power Query Group By to pre-aggregate the transactions to one row per day before loading.
Set all relationships that involve the transaction table to single cross-filter direction.
Aggregating the transaction table to the grain actually required by reports (one row per day) removes most of the 120 million rows that the model must store and scan. Reducing table granularity lowers memory consumption proportionally and shortens VertiPaq storage engine scans, so DAX measures calculate faster. Switching to DirectQuery would cut memory use but typically slows query performance because each visual sends SQL to the source. Column profiling only affects the Power Query editor experience and not model performance. Changing cross-filter direction controls filter propagation but does not reduce table size or scan cost.
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What is Power Query Group By, and how does it help in pre-aggregating data?
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Why is switching to DirectQuery not recommended in this scenario?
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What is the VertiPaq storage engine, and how does reducing table granularity impact it?
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