Choosing the storage level involves considering various factors. You should consider the size of the RDD A, as large datasets might not fit entirely in memory. Prioritizing memory usage B improves access speed but might not be suitable for fault-tolerance. Balancing memory usage with disk persistence C ensures data availability even in case of node failures, offering a compromise between performance and fault tolerance.

All the options listed in D contribute to improving the readability and maintainability of Airflow DAGs. Using descriptive names, modularizing with sub-DAGs, and adding relevant logging practices enhance understanding and troubleshooting for future maintenance.

While increasing executors A might help, it's not the most targeted approach. Changing file format B might have downsides. Partition pruning C allows Spark to only access relevant data partitions based on the query, significantly reducing the amount of data scanned and improving efficiency. Custom compression D adds complexity and might not be the first optimization to consider.

While checking the code A might provide some insights, it's not comprehensive. The Spark web UI B offers general storage information. Spark metrics C provide detailed information about specific RDDs, including their storage level and size, allowing for effective monitoring and performance analysis.