When optimizing complex Spark SQL queries with multiple subqueries and aggregations, dividing the query into smaller, more manageable segments is the most effective strategy. This method enables Spark to optimize the execution plan for each segment individually, enhancing overall performance. It also simplifies identifying and addressing bottlenecks. Caching intermediate results that are reused can further boost performance by minimizing redundant computations. Conversely, consolidating all subqueries into one complex query may complicate the execution plan and hinder performance. Adjusting spark.sql.broadcastTimeout might offer some benefits, but it's not the optimal solution for complex queries. Similarly, enforcing disk operations with persist(StorageLevel.DISK_ONLY) could impair performance due to the slower nature of disk compared to memory operations. Thus, segmenting the query and caching reusable intermediate results stands out as the best approach for refining Spark SQL performance in complex scenarios.