Microsoft Fabric Data Engineer Associate DP-700 Practice Question
You manage a Microsoft Fabric warehouse. A 4-TB sales fact table is currently stored using the default clustered columnstore index and a ROUND_ROBIN distribution. Most analytical queries join this table with a 15-GB Date dimension and filter on the calendar year. The queries scan many unnecessary rows and exhibit high data movement during joins. Without purchasing additional capacity, which change is most likely to reduce both scan and shuffle costs?
Create a materialized view that filters the fact table to the current calendar year.
Convert the fact table to a clustered rowstore index to improve predicate pushdown on DateKey.
Redistribute the fact table by using HASH on the DateKey column while keeping the clustered columnstore index.
Replicate the fact table instead of distributing it so all compute nodes have a local copy.
Changing the sales fact table to a hash distribution on the surrogate key that joins to the Date dimension will colocate matching rows on the same distribution, eliminating the shuffle that currently occurs with a ROUND_ROBIN table. Because clustered columnstore remains in place, segment elimination will still prune rowgroups for the filtered calendar years, further reducing the amount of data scanned. Simply switching to a clustered index or replicating a 4-TB table is impractical due to size, and materializing the yearly subset would not remove the data-movement overhead of the join.
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Microsoft Fabric Data Engineer Associate DP-700
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