Microsoft Fabric Data Engineer Associate DP-700 Practice Question
A Fabric lakehouse contains a Delta table named order_transactions that stores 12 months of data. About 50 small Parquet files (10 MB each) are appended every day by an ingestion pipeline. Business analysts access the table through the SQL analytics endpoint, and almost every report applies a filter on the OrderDate column. Report performance has been degrading. You need to improve query latency without changing the ingestion process or modifying any reports. Which action should you perform first?
Run VACUUM on the Delta table with a 7-day retention period.
Execute OPTIMIZE order_transactions WITH (ZORDER = (OrderDate)).
Enable the property delta.autoOptimize.optimizeWrite on the table.
Create a cached view that selects only the needed columns, including OrderDate.
Running OPTIMIZE with ZORDER rewrites the existing small files into larger, co-located data files and physically orders the data by the specified column. This greatly increases data-skipping efficiency when queries filter on OrderDate, reducing I/O and improving response time. VACUUM only removes obsolete files and has little effect on current query speed. Enabling delta.autoOptimize.optimizeWrite affects only future writes and leaves the existing fragmented data unchanged. Creating and caching a view does not address the underlying file fragmentation and therefore offers limited benefit.
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Microsoft Fabric Data Engineer Associate DP-700
Monitor and optimize an analytics solution
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