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When designing a Spark job in Databricks for a multi-dimensional analysis project that involves complex joins across multiple large datasets, which strategies would you implement to optimize join operations for high performance?
A
Applying a filter on datasets prior to joining to reduce the size of data being processed and joined.
B
Converting datasets to Delta format and using Z-ordering to colocate related information on the disk for faster access during joins.
C
Pre-partitioning datasets based on join keys before executing the join operations to minimize data shuffling.
D
Utilizing broadcast joins for smaller datasets to avoid shuffling the larger dataset across the cluster.
