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Answer: Task Aggregation
Task Aggregation is the correct technique for combining smaller tasks into larger ones to minimize scheduling and communication overhead in Spark. This approach is particularly beneficial in distributed computing environments like Spark, where reducing overhead is key to optimizing performance. By aggregating tasks, Spark can process data more efficiently, leading to improved overall performance.
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In the context of optimizing Spark performance for a machine learning project, which technique is used to combine multiple small tasks into larger tasks to reduce scheduling overhead and enhance processing efficiency?
A
Task Decomposition
B
Task Fusion
C
Task Aggregation
D
Task Parallelism