Microsoft Fabric Data Engineer Associate DP-700 Practice Question
Your team ingests high-frequency IoT events into a Delta table named sensor_data in a Microsoft Fabric lakehouse. The pipeline appends thousands of 10-KB files every hour. Analysts run queries such as SELECT * FROM sensor_data WHERE device_id = 'abc' and say latency has grown to several minutes. You must improve query performance without changing the ingestion pattern. Which action should you perform?
Enable Change Data Feed by setting the table property delta.enableChangeDataFeed = true.
Run VACUUM sensor_data RETAIN 0 HOURS to remove obsolete files immediately.
Run OPTIMIZE sensor_data ZORDER BY (device_id) in a Fabric notebook.
Export the table to plain Parquet files and reload it with COPY INTO.
Running the OPTIMIZE command compacts many small files into fewer large files, reducing metadata overhead. When you add the ZORDER BY clause on device_id, the data is reorganized to colocate rows that share the same device_id, improving data skipping and filter pruning for selective queries. VACUUM only removes obsolete files and does not address the small-file problem or data clustering. Converting to plain Parquet removes Delta Lake features and still leaves many small files, while enabling Change Data Feed benefits auditing rather than query speed. Therefore, using OPTIMIZE with ZORDER is the correct choice.
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What is the ZORDER BY clause in Delta Lake and how does it improve query performance?
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What does the OPTIMIZE command do in a Delta table?
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Why is VACUUM not the right choice for solving latency issues in Delta Lake tables?
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Microsoft Fabric Data Engineer Associate DP-700
Monitor and optimize an analytics solution
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