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When optimizing a Spark DataFrame operation for large-scale aggregations on a dataset with a billion rows, which strategy would you employ to reduce processing time and enhance resource utilization?
A
Utilizing columnar storage formats like Parquet to improve scan efficiency during aggregation
B
Applying the coalesce method to reduce the number of partitions before aggregation
C
Implementing custom partitioning to ensure even distribution of data across nodes before aggregation
D
Leveraging broadcast variables to minimize data shuffling during join operations
