Microsoft Fabric Data Engineer Associate DP-700 Practice Question
Your Microsoft Fabric warehouse has two large fact tables-FactSales (5 billion rows) and FactInventory (4 billion rows)-plus small dimensions such as DimDate (~50 000 rows). All tables use ROUND_ROBIN distribution. An equi-join between the fact tables on ProductKey shows heavy data movement. To minimize movement and keep dimension joins fast, what should you change?
Keep ROUND_ROBIN distribution for all tables but enable result-set caching on the warehouse.
Set FactSales and FactInventory to REPLICATE distribution and change DimDate to HASH on DateKey.
Redistribute FactSales by HASH on SalesOrderNumber and set FactInventory to REPLICATE; leave DimDate as ROUND_ROBIN.
Redistribute FactSales and FactInventory by HASH on ProductKey, and change DimDate to REPLICATE distribution.
Hash-distributing both FactSales and FactInventory on ProductKey aligns rows with identical keys on the same distribution, so the join can occur locally without shuffling data. Because DimDate is tiny, setting it to REPLICATE copies the table to every compute node, eliminating movement during joins with either fact table. Leaving the facts as ROUND_ROBIN, hashing on an unrelated column, or replicating multi-billion-row tables would all continue to incur high movement or create excessive overhead.
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What is ROUND_ROBIN distribution in Microsoft Fabric?
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Why is REPLICATE distribution used for small tables like dimensions?
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Microsoft Fabric Data Engineer Associate DP-700
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