Understand the Features of Liquid Clustering and Predictive Optimization. DATA-ENGINEER-ASSOCIATE Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A data team manages several Delta tables that are updated frequently. They want to automate maintenance tasks such as compaction and clustering to reduce manual effort and ensure consistent query performance. Which feature should they use?

   • ADelta Live Tables
   • BAuto Loader
   • CLiquid Clustering
   • DPredictive optimization
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2. 2foundation · easy

   Which maintenance tasks can predictive optimization automate?

   • ACREATE TABLE and DROP TABLE
   • BMERGE and UPDATE
   • COPTIMIZE and VACUUM
   • DANALYZE TABLE and DESCRIBE TABLE
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4. 3application · medium

   A data platform team is responsible for managing hundreds of Delta tables. They want to reduce the time spent on manual maintenance tasks like compaction and vacuum. They also want to ensure that tables with high query activity are optimized more frequently than those with low activity. What should they implement?

   • AEnable Liquid Clustering on all tables.
   • BCreate a Databricks job to run OPTIMIZE on all tables every hour.
   • CEnable predictive optimization on the tables that need it.
   • DRun OPTIMIZE and VACUUM on all tables nightly.
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5. 4application · medium

   A data engineer is designing a Delta table that will store IoT sensor readings. The table will be appended continuously and queried by `device_id` and `timestamp`. The team wants to minimize query latency and avoid frequent manual maintenance. Which approach should the engineer choose?

   • AEnable Liquid Clustering with clustering keys on `device_id` and `timestamp`.
   • BUse Z-ordering on `device_id` and `timestamp` after each append.
   • CStore the data as Parquet files without any clustering.
   • DPartition the table by `device_id` and `timestamp`.
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6. 5foundation · easy

   Which statement best describes how Liquid Clustering manages data layout in Delta Lake?

   • AIt requires a fixed set of clustering keys that cannot be changed after table creation
   • BIt uses a single clustering key that must be a high-cardinality column
   • CIt only works on tables that are partitioned by date
   • DIt incrementally updates data layout as new data is written, without requiring a full table rewrite
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