While reviewing a query ' s execution in the Databricks Query Profiler, a data engineer...

When Spark performs wide transformations such as sortBy or orderBy, large data volumes can exceed memory limits, causing disk spilling . The official Databricks performance tuning guide recommends increasing the shuffle partition count to distribute the data more evenly across executors. By default, Spark uses a fixed number of shuffle partitions (e.g., 200), which can lead to memory imbalance and spill if some partitions are too large. Increasing this number (via spark.sql.shuffle.partitions) results in smaller partitions, reduced in-memory pressure, and improved sort performance. Other options like broadcast joins or single partition sorts do not apply to single-table sorts, and converting to filters changes query logic. Thus, option D is the correct remedy.