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Answer: The application is experiencing data skew; analyze and repartition the data.
Slow tasks could be due to data skew, where certain tasks are processing disproportionately larger amounts of data. Analyzing and repartitioning the data can help distribute the load more evenly across tasks, thereby improving performance.
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You are monitoring a Spark application and notice that the Spark UI shows a high number of slow tasks in a specific stage. What could be the potential causes of these slow tasks, and how would you address them?
A
The application is experiencing data skew; analyze and repartition the data.
B
The application is running out of memory; increase the memory allocation for executors.
C
The application is encountering network issues; check and optimize network configurations.
D
The application is performing complex computations; increase the task parallelism.
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