Understand Delta Optimization Techniques, Such as Deletion Vectors and Liquid Clustering. DATA-ENGINEER-PROFESSIONAL Practice Questions (Page 3)

Questions 11–15

1. 11application · medium

   A data engineer is comparing liquid clustering with traditional partitioning for a Delta table that is filtered on a `date` column and a `product_id` column. The table is updated with new data throughout the day. What is a key advantage of liquid clustering over partitioning in this scenario?

   • ALiquid clustering guarantees faster queries than partitioning for all workloads.
   • BLiquid clustering eliminates the need for file compaction.
   • CLiquid clustering can adapt to changing query patterns without requiring a table rewrite.
   • DLiquid clustering automatically partitions the table by date.
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2. 12foundation · easy

   How does a deletion vector affect a read operation on a Delta table?

   • AThe reader skips the entire file if any row is marked as deleted
   • BThe reader automatically rewrites the file to remove deleted rows
   • CThe reader only reads the deletion vector and not the data file
   • DThe reader reads the data file and applies the deletion vector to filter out deleted rows
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4. 13application · medium

   A data engineer is evaluating liquid clustering for a Delta table that is updated with new data every hour. The team wants to avoid manual maintenance tasks. What is a key benefit of liquid clustering in this scenario?

   • AIt automatically deletes old data based on retention policies.
   • BIt guarantees that all queries will have the same performance.
   • CIt automatically reorganizes data files as new data arrives, reducing the need for manual OPTIMIZE runs.
   • DIt eliminates the need for any file compaction.
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5. 14expert · hard

   A Delta table has deletion vectors enabled. A user runs a `MERGE` operation that updates 20% of the rows. After the operation, the table size has not decreased, and query performance has degraded. What is the most likely cause?

   • AThe merge operation failed and left the table in an inconsistent state.
   • BThe merge operation increased the number of files without deleting any old files.
   • CThe merge operation rewrote all files, but the table is not optimized.
   • DThe merge operation created new files and marked old rows as deleted using deletion vectors, leaving the old files in place.
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6. 15application · medium

   A company has a Delta table that stores IoT sensor readings. The table is frequently updated with new readings, and occasionally a small set of erroneous readings must be deleted. The team wants to minimize the time and storage cost of these deletes. Which approach should they use?

   • AUse liquid clustering with a clustering key on the sensor ID to improve delete performance.
   • BEnable deletion vectors on the table and rely on them for the occasional deletes.
   • CUse Z-ordering on the timestamp column to speed up deletes.
   • DPerform a full table rewrite after each delete to keep the table optimized.
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