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Exam Certified Associate Developer for Apache Spark All Questions

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Exam Certified Associate Developer for Apache Spark topic 1 question 3 discussion

Which of the following will occur if there are more slots than there are tasks?

  • A. The Spark job will likely not run as efficiently as possible.
  • B. The Spark application will fail – there must be at least as many tasks as there are slots.
  • C. Some executors will shut down and allocate all slots on larger executors first.
  • D. More tasks will be automatically generated to ensure all slots are being used.
  • E. The Spark job will use just one single slot to perform all tasks.
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Suggested Answer: A 🗳️

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TC007
Highly Voted 2 years ago
Selected Answer: A
Slots are the basic unit of parallelism in Spark, and represent a unit of resource allocation on a single executor. If there are more slots than there are tasks, it means that some of the slots will be idle and not being used to execute any tasks, leading to inefficient resource utilization. In this scenario, the Spark job will likely not run as efficiently as possible. However, it is still possible for the Spark job to complete successfully. Therefore, option A is the correct answer.
upvoted 6 times
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KM16494
Most Recent 2 weeks, 2 days ago
Selected Answer: A
A is correct
upvoted 1 times
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zic00
8 months, 3 weeks ago
Selected Answer: A
If there are more slots (i.e., available cores) than tasks, some of the slots will remain idle, leading to underutilization of resources. This can result in less efficient execution because the available resources are not being fully utilized.
upvoted 1 times
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Raheel_te
10 months, 1 week ago
A is correct
upvoted 1 times
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SnData
11 months, 1 week ago
Answer - A
upvoted 1 times
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tzj_d
1 year, 1 month ago
Selected Answer: A
it is A
upvoted 1 times
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zozoshanky
1 year, 4 months ago
C. Some executors will shut down and allocate all slots on larger executors first. explanation :If there are more slots than there are tasks in Apache Spark, some executors may shut down, and the available slots will be allocated to larger executors first. This process is part of the dynamic resource allocation mechanism in Spark, where resources are adjusted based on the workload. It helps in efficient resource utilization by shutting down unnecessary executors and allocating resources to larger executors to perform tasks more efficiently.
upvoted 2 times
raghavendra516
9 months, 2 weeks ago
there is dynamic property : spark.dynamicAllocation.executorIdleTimeout which reallocate executors when they are idle
upvoted 1 times
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knivesz
1 year, 4 months ago
Selected Answer: E
E , When there are more available slots than tasks, Spark will use a single slot to perform all tasks, which may result in inefficient use of resources.
upvoted 1 times
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knivesz
1 year, 4 months ago
Selected Answer: E
E es Correct
upvoted 1 times
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hua
1 year, 4 months ago
Selected Answer: A
A is correct
upvoted 1 times
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astone42
1 year, 9 months ago
Selected Answer: A
A is correct
upvoted 1 times
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singh100
1 year, 9 months ago
A. IF there are more slots than there are tasks, the extra slots will not be utilized, and they will remain idle, resulting in some resource waste. To maximize resource usage efficiency, it is essential to configure the cluster properly and adjust the number of tasks and slots based on the workload demands. Dynamic resource allocation features in cluster managers can also help improve resource utilization by adjusting the cluster size dynamically based on the task requirements.
upvoted 1 times
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4be8126
2 years ago
Selected Answer: A
A. The Spark job will likely not run as efficiently as possible. In Spark, a slot represents a unit of processing capacity that an executor can offer to run a task. If there are more slots than there are tasks, some of the slots will remain unused, and the Spark job will likely not run as efficiently as possible. Spark automatically assigns tasks to slots, and if there are more slots than necessary, some of them may remain idle, resulting in wasted resources and slower job execution. However, the job will not fail as long as there are enough resources to execute the tasks, and Spark will not generate more tasks than needed. Also, executors will not shut down because there are unused slots. They will remain active until the end of the job or until explicitly terminated.
upvoted 4 times
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