Answer: storesDF.union(), storesDF.coalesce(1)

The correct operations are union() and coalesce(1). - **C. union()**: Combines two DataFrames by appending rows without shuffling data. It is a narrow transformation as it simply stacks partitions. - **D. coalesce(1)**: Reduces partitions by merging existing ones without a full shuffle. Coalesce avoids shuffling by combining partitions locally where possible, even though moving to a single partition may involve data movement, it does not induce a shuffle (unlike repartition). Other options: - **A. intersect()**: Requires identifying common rows, which typically triggers a shuffle. - **B. repartition(1)**: Explicitly redistributes data via a shuffle. - **E. rdd.getNumPartitions()**: Returns an integer (number of partitions), not a DataFrame.

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