When creating an Iceberg table in watsonx.data to store daily transaction data; what partitioning strategy would most improve query performance for date-range queries?

Partitioning by date (year/month/day) allows Presto to skip entire partitions when evaluating date-range predicates; reducing the number of files scanned and dramatically improving query performance. The incorrect options ("Partition by a hash of the transaction ID to evenly distribute rows", "Do not partition the table; let Presto's CBO determine the optimal scan strategy", "Partition by a random UUID column to avoid data skew") are distractors that don't fully capture the concept described.