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Answer: Chain transformations together before executing any actions to leverage Spark‘s lazy evaluation.
The correct approach for efficient execution when processing a large DataFrame in Spark and applying multiple transformations is to chain transformations together before executing any actions to leverage Spark‘s lazy evaluation. This method allows Spark to optimize the execution plan by building a directed acyclic graph (DAG) of transformations, which is executed in the most efficient way possible when an action is called. Chaining transformations minimizes unnecessary computations and data shuffling, enhancing performance. While caching intermediate results (option C) can improve performance, it's not always necessary and may lead to excessive memory use. Using UDFs (option A) can aid in readability for complex transformations but isn't required for all cases and may introduce overhead. Executing actions after each transformation (option D) is useful for debugging but inefficient due to redundant computations. Therefore, chaining transformations before actions is the optimal strategy for efficiency and maintainability.
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When processing a large DataFrame in Spark with multiple transformations, which method ensures the most efficient execution?
A
Use UDFs for all transformations to improve readability and maintainability.
B
Chain transformations together before executing any actions to leverage Spark‘s lazy evaluation.
C
Apply transformations sequentially, and call .cache() after each transformation.
D
Execute an action after each transformation to immediately view results and ensure correctness.
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