Microsoft Fabric Data Engineer Associate DP-700 Practice Question
You manage a Microsoft Fabric lakehouse that receives continuous IoT telemetry. The streaming job appends thousands of small Parquet files to a Delta table named SensorEvents every hour. Data analysts report that interactive queries against SensorEvents have become significantly slower over time. You must improve query performance without changing the existing ingestion process or creating a duplicate copy of the data. Which action should you perform on the table?
Execute an OPTIMIZE operation on SensorEvents to compact small files into larger files.
Run ANALYZE TABLE SensorEvents COMPUTE STATISTICS to refresh query optimizer statistics.
Export SensorEvents to a new table partitioned by ingestion hour and point reports to the new table.
Create row-level indexes on the most frequently filtered columns in SensorEvents.
Running the OPTIMIZE command on a Delta table rewrites many small files into a smaller number of larger files. In Microsoft Fabric this file compaction reduces the overhead that the execution engine incurs when it must open and read large numbers of tiny files, thereby accelerating scan-intensive analytic queries. Row-level indexing is not available in Delta Lake, COMPUTE STATISTICS only updates the optimizer's metadata without reducing file counts, and moving data to a new partitioned table would require creating and managing another physical copy of the dataset rather than improving the existing table's structure.
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Why does compacting files with the OPTIMIZE command improve query performance?
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What is the difference between OPTIMIZE and ANALYZE TABLE COMPUTE STATISTICS?
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What is a Delta table and why is it used for IoT telemetry?
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Microsoft Fabric Data Engineer Associate DP-700
Monitor and optimize an analytics solution
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