Microsoft Fabric Data Engineer Associate DP-700 Practice Question
You manage a lakehouse in Microsoft Fabric. The Delta table named Sales is appended to every few minutes, creating thousands of small data files. Business analysts report that queries which filter on the ProductCategory column are increasingly slow. You must improve scan performance and reduce file fragmentation without changing the ingestion pipeline design. Which action should you perform?
Export the Sales table to a single compressed CSV file and query that file instead of the Delta table.
Run VACUUM Sales RETAIN 0 HOURS to delete obsolete small files.
Run the command OPTIMIZE Sales ZORDER BY (ProductCategory) from a Fabric notebook.
Create a columnstore index on the ProductCategory column.
Running the OPTIMIZE command with ZORDER rewrites the Delta Lake table into a smaller number of large, coalesced Parquet files and physically clusters the data on the specified column. This reduces the number of files the query engine must open and enables data-skipping when predicates filter on ProductCategory, thereby lowering scan time and improving performance. Creating a columnstore or other traditional index is not supported on lakehouse Delta tables. VACUUM only removes files that are no longer referenced by the transaction log; it does not combine small files or organize data, so it has little impact on filter performance. Exporting to a single CSV file would eliminate Delta Lake features such as ACID transactions and would not be a maintainable optimization approach in Fabric.
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Microsoft Fabric Data Engineer Associate DP-700
Monitor and optimize an analytics solution
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