Answer: storesDF.repartition(), storesDF.intersect(otherDF)

The question asks for operations that induce a shuffle and return a new DataFrame with updated partitions. - **A. coalesce()**: Reduces partitions without a shuffle (merges existing partitions), so it does not induce a shuffle. - **B. rdd.getNumPartitions()**: Retrieves the partition count but does not modify the DataFrame or trigger a shuffle. - **C. repartition()**: Always induces a shuffle to redistribute data and creates a new DataFrame with the specified number of partitions. - **D. union()**: Combines DataFrames by appending rows, which does not require a shuffle (partitions are concatenated). - **E. intersect()**: Requires a shuffle to compare rows across all partitions and find common elements, resulting in a new partitioned DataFrame. Thus, **C** and **E** are correct as they involve shuffling and produce new partitions.

Author: LeetQuiz Editorial Team