Understand the Features of Liquid Clustering and Predictive Optimization. DATA-ENGINEER-ASSOCIATE Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   What is the role of OPTIMIZE in Liquid Clustering?

   • AIt incrementally reorganizes data to improve clustering, working alongside automatic clustering during writes
   • BIt performs a one-time full rewrite of the table to apply clustering keys
   • CIt is required to enable Liquid Clustering on a table
   • DIt drops all existing clustering keys and resets the table to an unclustered state
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2. 17foundation · easy

   Which Databricks feature can you use to monitor the effects of Liquid Clustering and predictive optimization?

   • AUnity Catalog lineage
   • BCluster event logs
   • CQuery history
   • DJob run details
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4. 18application · medium

   A company has enabled Liquid Clustering and predictive optimization on a large Delta table. They want to understand the cost and performance benefits of these features. What is the best way to assess the impact?

   • ARun a full table scan and measure the time.
   • BReview the table's schema and clustering keys.
   • CCheck the number of files in the table directory.
   • DCompare query runtime and cost metrics before and after enabling the features.
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5. 19foundation · easy

   What is the primary purpose of predictive optimization in Databricks?

   • ATo predict future query performance and automatically scale compute resources
   • BTo replace the need for manual OPTIMIZE and VACUUM commands entirely
   • CTo automatically rewrite all queries to use optimal join strategies
   • DTo automatically run maintenance tasks like compaction and clustering based on table usage patterns
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6. 20application · medium

   After enabling Liquid Clustering and predictive optimization on a Delta table, a data engineer wants to verify that the features are working as expected. Which approach should they use?

   • ACheck the table's history and metrics in the Databricks UI, and review query performance before and after the changes.
   • BRun `DESCRIBE TABLE table_name` and look for the `clusteringColumns` field.
   • CRun `OPTIMIZE table_name` and check the number of files before and after.
   • DQuery the `information_schema.tables` view to see if predictive optimization is enabled.
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