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Answer: Optimize the job by adjusting the memory and CPU allocation to better match the workload.
Option B is the correct approach as it involves a holistic optimization of the job by adjusting both memory and CPU allocation to better match the workload, which can help in managing resources more effectively. Option A might not be sufficient if the CPU is also a bottleneck. Option C is a more drastic measure that may not be necessary if the job can be optimized. Option D can be part of the solution but may not address the root cause of the data spill if it's due to excessive memory usage rather than data size.
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Your data processing job is experiencing data spill, where intermediate data exceeds the memory capacity of the nodes in the cluster. What steps would you take to handle this situation and optimize resource management?
A
Increase the memory allocation for the job without changing any other parameters.
B
Optimize the job by adjusting the memory and CPU allocation to better match the workload.
C
Add more nodes to the cluster to increase the overall memory capacity.
D
Implement data compression to reduce the size of the intermediate data.
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