Answer: Applying salting techniques to distribute the skewed data more evenly before the join.

**B. Applying salting techniques to distribute the skewed data more evenly before the join.** - **A. Forcing a shuffle partition increase using spark.sql.shuffle.partitions:** While increasing shuffle partitions can distribute data more evenly, it's not the most efficient solution for skew during joins, potentially leading to higher resource use and longer processing times. - **C. Implementing broadcast joins for all operations irrespective of data size:** Broadcast joins are optimal for small tables that fit in memory across all nodes. Using them universally, especially for large tables, can degrade performance and doesn't address skew effectively. - **D. Utilizing the OPTIMIZE command on tables post-join to improve subsequent query performance:** The OPTIMIZE command enhances data file layout for better query performance but doesn't directly resolve skew issues during the join operation. - **B. Applying salting techniques:** Salting involves adding a random or hashed prefix to keys, distributing skewed data more evenly across partitions. This method directly tackles skew, optimizing join operations and improving query performance.

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