Compare Tableau blending, joins, and filters

You have two datasets to analyze in Tableau. Primary data source: Orders - Columns: OrderID, State, Category, Sales - Rows: 1) (O1, CA, Furniture, 100) 2) (O2, CA, Office Supplies, 200) 3) (O3, NY, Technology, 150) 4) (O4, TX, Furniture, 300) 5) (O5, CA, Technology, 50) Secondary data source: Targets - Columns: State, Category, MonthlyTarget - Rows: A) (CA, Furniture, 500) B) (CA, Technology, 400) C) (NY, Furniture, 250) D) (FL, Technology, 600) Answer the following precisely: 1) If you perform a physical inner join on State and Category, which rows remain and what are the values of SUM(Sales) and SUM(MonthlyTarget) by State, Category? Repeat for a left join (Orders → Targets). 2) If instead you blend the Targets source into Orders (Orders = primary), explain exactly how Tableau computes the view when you place State and Category on rows and SUM(Sales), SUM(MonthlyTarget) on columns. Specify how unmatched members (e.g., NY–Technology; TX–Furniture; FL–Technology) appear, and whether their targets/sales show as null/zero. Why? 3) Show how the results change under each of these filters and explain why based on Tableau’s order of operations: (a) a dimension filter Category = 'Furniture' from the Orders source; (b) a dimension filter Category = 'Furniture' from the Targets source; (c) a context filter State = 'CA' on Orders; (d) a measure filter SUM(Sales) > 150 applied to the view; (e) a data source filter on Targets keeping only Category IN ('Furniture','Technology'). 4) Describe two pitfalls where blending and joining yield different aggregates due to level of detail and many-to-many relationships (e.g., duplicate State–Category rows in Targets). Provide a concrete example from the given data. 5) When would you prefer blending over joining in Tableau? Discuss cross-database analysis, required granularity, the linking field behavior, impacts on performance/caching, and limitations with calculations (e.g., FIXED LODs, table calcs) in secondary sources.