Answer: Result Caching

**Result Caching** is the correct answer. This technique involves precomputing and storing intermediate results to minimize computational overhead in later tasks. In Spark, caching is especially beneficial for avoiding the recomputation of the same data or intermediate results across multiple tasks or actions that depend on the same dataset. By storing intermediate data in memory, Spark makes it quickly accessible for subsequent operations, eliminating the need to recompute from the original data source. This approach can dramatically enhance performance by cutting down computation time and lowering the overall processing cost. **Lazy Evaluation** refers to Spark's strategy of delaying transformation execution until an action is initiated. **Task Fusion** combines smaller tasks into larger ones to decrease overhead, while **Data Compression** focuses on reducing data size for storage or transmission purposes. Although these methods are relevant for optimization, Result Caching specifically targets the reuse of intermediate results.

Author: LeetQuiz Editorial Team