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Consider a scenario where you need to process a large volume of semi-structured data using Apache Spark in a cloud environment. The data includes log files from multiple servers and needs to be transformed into a structured format for analysis. Describe the steps you would take to achieve this, including how you would optimize the Spark jobs for performance and cost efficiency.
A
Use Spark's DataFrame API for transformations and ignore any performance tuning.
B
Manually partition the data before loading into Spark to optimize processing.
C
Use Spark's DataFrame API for transformations, apply appropriate partitioning, and utilize Spark's caching and checkpointing features to optimize performance.
D
Convert all data to CSV before processing in Spark to simplify the transformation process.