Caching intermediate results with rdd.cache() B provides efficient access and updates between iterations, as data resides in memory, minimizing disk 1/0. While temporary tables A and custom data structures C might work, they can be less efficient for iterative processing. Broadcast variables D are suitable for frequently accessed data across all iterations, not specifically for intermediate results.

Schema inference primarily addresses the challenge of dealing with the variety and complexity of data formats and structures inherent in big data ecosystems. By automatically determining the structure of data, schema inference allows for more flexible and efficient processing of diverse datasets without the need for predefined schemas, thus tackling the issue of data heterogeneity.

When leveraging Spark's DataFrame API, Spark performs several implicit optimizations, including predicate pushdown, where it moves filter operations closer to the data source, thereby reducing the amount of data shuffled or moved across the network. This optimization, part of the Catalyst optimizer's logical optimization phase, enhances processing efficiency by minimizing the data processed in subsequent stages of the query.

Setting the start date to a future date using the datetime module ensures that the DAG will not trigger immediately upon deployment but will instead start at the specified future date. This allows for controlled execution and avoids unexpected runs during deployment.

Option C is the correct choice because it uses the 'spark.sql' method to execute a SQL query on a Hive table.