Simplify Data Layout Decisions and Optimize Query Performance Using Liquid Clustering. DATA-ENGINEER-PROFESSIONAL Practice Questions (Page 2)

Questions 6–10

1. 6application · medium

   A Delta table stores user activity logs with columns `user_id`, `activity_type`, and `event_time`. Queries often filter by `user_id` and `event_time`, but the table was originally created without clustering. The team wants to improve query performance without rewriting the table. What should they do?

   • ARecreate the table with CLUSTER BY (user_id, event_time) and copy all data.
   • BCreate an index on user_id and event_time.
   • CRun OPTIMIZE ZORDER BY (user_id, event_time);
   • DRun ALTER TABLE ... CLUSTER BY (user_id, event_time);
   Reveal Solution Discussion Select an answer first
2. 7foundation · easy

   What is the primary purpose of Liquid Clustering in Delta Lake?

   • ATo encrypt data at rest and in transit for enhanced security
   • BTo provide real-time streaming ingestion capabilities for Delta tables
   • CTo automatically optimize data layout for better query performance without manual tuning
   • DTo replace the need for partitioning entirely by storing all data in a single file
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4. 8application · medium

   A team manages a Delta table that is updated frequently with new records. They have been manually running OPTIMIZE and ZORDER BY to maintain query performance, but this is time-consuming and often forgotten. They want to reduce manual intervention while still improving data skipping for common filters. What is the most effective approach?

   • AIncrease the number of partitions to reduce the amount of data scanned per query.
   • BEnable Liquid Clustering on the table and let it automatically manage layout during write operations.
   • CUse a materialized view to pre-aggregate the data for common queries.
   • DSchedule a nightly job to run OPTIMIZE and ZORDER BY on the table.
   Reveal Solution Discussion Select an answer first
5. 9application · medium

   A Delta table was created with CLUSTER BY (customer_id). After a change in business requirements, queries now frequently filter by `order_status` and `order_date`. The team wants to update the clustering keys without rewriting the entire table. What should they do?

   • ARecreate the table with the new clustering keys and copy all data.
   • BRun OPTIMIZE with ZORDER BY (order_status, order_date);
   • CDrop the table and create a new one with the updated clustering keys.
   • DRun ALTER TABLE ... CLUSTER BY (order_status, order_date);
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6. 10foundation · easy

   Which Delta Lake feature is NOT compatible with Liquid Clustering?

   • ATime travel
   • BSchema evolution
   • CZ-ORDER BY optimization
   • DChange Data Feed
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