Microsoft Fabric Data Engineer Associate DP-700 Practice Question
You run analytics queries against a Delta table stored in a Microsoft Fabric lakehouse. After weeks of streaming upserts, you discover the table path now contains thousands of small Parquet files, and scan latency has noticeably increased when filtering on the OrderDate column. You need to consolidate the files and improve data-skipping without rewriting the ingestion pipeline. Which action should you take?
Enable Change Data Feed (CDF) on the table to reduce query latency.
Run VACUUM on the table with a zero-hour retention period to delete all old files.
Create a clustered columnstore index on the table to accelerate scans on OrderDate.
Execute an OPTIMIZE statement on the table with a ZORDER BY (OrderDate) clause to compact and co-locate data.
The OPTIMIZE command rewrites a Delta table into a smaller set of large files, eliminating the many small files that accumulate during frequent inserts or merges. Adding the ZORDER BY clause sorts the data on the specified column-in this case OrderDate-so predicate push-down can skip entire data ranges, further reducing scan time. Enabling Change Data Feed only exposes row-level changes and does not compact files. VACUUM removes obsolete files but leaves current small files untouched, so it has little effect on query speed. Clustered columnstore indexes are an Azure Synapse dedicated SQL pool feature and are not supported for lakehouse Delta tables in Fabric.
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Microsoft Fabric Data Engineer Associate DP-700
Monitor and optimize an analytics solution
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