Simplify Data Layout Decisions and Optimize Query Performance Using Liquid Clustering. DATA-ENGINEER-PROFESSIONAL Practice Questions (Page 4)

Questions 16–20

1. 16application · medium

   A data platform team is evaluating ways to improve query performance on a large Delta table. They currently use manual partitioning and Z-ordering, but the data distribution changes frequently, and maintenance is becoming a burden. They want a solution that reduces manual effort and adapts to changing data patterns. Which approach best meets their needs?

   • ASwitch to a columnar file format that does not require layout management.
   • BIncrease the frequency of OPTIMIZE and ZORDER BY jobs.
   • CUse a separate table for each common query filter.
   • DAdopt Liquid Clustering with clustering keys that match common query filters.
   Reveal Solution Discussion Select an answer first
2. 17foundation · easy

   Which SQL clause is used to specify clustering keys when creating a Delta table with Liquid Clustering?

   • AORDER BY
   • BPARTITION BY
   • CGROUP BY
   • DCLUSTER BY
   Reveal Solution Discussion Select an answer first
4. 18foundation · easy

   Which SQL command is used to alter clustering keys on an existing Delta table?

   • AALTER TABLE ... PARTITION BY
   • BMODIFY TABLE ... CLUSTERING
   • CALTER TABLE ... CLUSTER BY
   • DUPDATE TABLE ... SET CLUSTER
   Reveal Solution Discussion Select an answer first
5. 19foundation · easy

   What does 'automatic layout management' mean in Liquid Clustering?

   • AThe system automatically creates partitions based on data values
   • BThe system automatically adjusts the physical layout of data as new data is written
   • CThe user must manually run Z-ORDER BY after every write
   • DThe user must specify partition columns for every table
   Reveal Solution Discussion Select an answer first
6. 20application · medium

   A company stores IoT sensor readings in a Delta table. Queries frequently filter on `device_id` and `timestamp`, but the data is ingested in near-real-time from multiple streams. The team has tried manual partitioning by date and Z-ordering on `device_id`, but maintenance is cumbersome and query performance degrades as new partitions are added. They want a simpler layout that adapts automatically to changing data patterns. What should they do?

   • AKeep the current partitioning and add more Z-order columns to cover all query filters.
   • BRecreate the table with CLUSTER BY (device_id, timestamp) using Liquid Clustering.
   • CUse a view that filters data on the fly without changing the physical layout.
   • DSwitch to a Delta table partitioned by device_id and timestamp.
   Reveal Solution Discussion Select an answer first