Microsoft Fabric Data Engineer Associate DP-700 Practice Question
You manage a Microsoft Fabric lakehouse table named Clicks that is stored in Delta format and contains about 100 billion rows. Data is appended every day. Analysts complain that interactive SQL queries that filter on the EventDate and UserId columns take too long because many files are scanned. You want to physically reorganize the existing data so that future queries that filter on EventDate read as few files as possible, without changing the table schema. What should you do?
Run ANALYZE TABLE Clicks COMPUTE STATISTICS for all columns.
Set the table property delta.autoOptimize.optimizeWrite = true to enable automatic file compaction.
Cache the table in memory by executing CACHE TABLE Clicks.
Run an OPTIMIZE Clicks ZORDER BY (EventDate) command.
The OPTIMIZE command with a ZORDER BY clause rewrites existing Delta files so that rows with similar values for the chosen column(s) are colocated. This clustering allows the Delta Lake data-skipping indexes to eliminate whole files during predicate push-down, reducing the volume read when queries filter on EventDate. Enabling delta.autoOptimize.optimizeWrite only affects new writes and does not reorganize existing data. ANALYZE TABLE gathers statistics for the cost-based optimizer but does not change the physical layout of the data. Caching keeps data in memory for faster access but still requires scanning every cached file, offering no benefit if the main issue is reading too many files.
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Microsoft Fabric Data Engineer Associate DP-700
Monitor and optimize an analytics solution
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