A developer is running Spark SQL queries and notices underutilization of resources.

The number of tasks is controlled by the number of partitions. By default, spark.sql.shuffle.partitions is 200. If stages are showing very few tasks (less than total cores), you may not be leveraging full parallelism.

From the Spark tuning guide:

"To improve performance, especially for large clusters, increase spark.sql.shuffle.partitions to create more tasks and parallelism."

Thus:

A is correct: increasing shuffle partitions increases parallelism

B is wrong: it further reduces parallelism

C is invalid: increasing dataset size doesn’t guarantee more partitions

D is irrelevant to task count per stage

Final Answer: A