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When tackling performance bottlenecks in complex data transformation jobs within Azure Databricks, which advanced optimization strategies can you employ?
A
Leveraging Databricks Delta Lake's optimization capabilities, including Z-Ordering and data skipping, to enhance performance on extensive datasets
B
Reviewing UDF applications thoroughly and potentially converting them to native Spark SQL functions or rewriting them in a more efficient language such as Scala
C
Adopting adaptive query execution functionalities in the latest Databricks runtimes to adjust query plans dynamically based on real-time statistics
D
Examining execution plans via Databricks' Spark UI to spot stages with uneven execution times and applying specific optimizations like broadcast joins or caching
