✅ B. Ensure that the parquet files are at least 1 GB in size.

Spark performs optimally with larger files. Processing files in the 200-400 MB range may lead to increased time spent on task scheduling and metadata management rather than actual data processing. By increasing the Parquet file size to approximately 1 GB, the number of tasks is reduced, leading to larger tasks that decrease overhead and enhance parallelism. This approach directly improves performance related to the initial data format without necessitating costly infrastructure changes.

❌ A. Switch from using parquet files to TFRecords formats, which are approximately 200 MB per file.

TFRecords is typically used with TensorFlow, whereas Parquet's columnar storage format is more suited for Spark analytical workloads due to its efficient data compression and predicate pushdown capabilities. Switching to TFRecords, especially without increasing file sizes, is unlikely to enhance performance and may even reduce it due to format incompatibility and the absence of columnar optimizations.

❌ C. Change from using HDDs to SSDs, copy initial data from GCS to HDFS, run the Spark job, and copy the results back to GCS.

While SSDs offer faster I/O than HDDs, potentially improving shuffle performance, they also increase the Dataproc cluster's cost. The question highlights cost sensitivity. Additionally, copying data from GCS to HDFS introduces unnecessary complexity and storage costs, as Dataproc is optimized to work efficiently with data directly on GCS.

❌ D. Change from using HDDs to SSDs and modify the configuration of preemptible VMs to increase the boot disk size.

SSDs increase costs, and enlarging the boot disk size of preemptible VMs does not directly address the inefficiencies related to shuffling performance or initial data format. Preemptible VMs are cost-effective for compute tasks but are not suitable for persistent storage or critical data management.