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Which strategy is most effective for reducing shuffling and enhancing query performance when optimizing a Spark job that processes a large Delta Lake table?
A
Using broadcast variables to minimize data transfer
B
Maintaining default partitioning to let Spark decide
C
Decreasing the number of partitions using coalesce()
D
Increasing the number of partitions to maximize parallelism
