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A data engineer is inspecting an ETL pipeline based on a Pyspark job that consistently...

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A data engineer is inspecting an ETL pipeline based on a Pyspark job that consistently encounters performance bottlenecks. Based on developer feedback, the data engineer assumes the job is low on compute resources. To pinpoint the issue, the data engineer observes the Spark Ul and finds out the job has a high CPU time vs Task time.

Which course of action should the data engineer take?

A.

High CPU time vs Task time means an under-utilized cluster. The data engineer may need to repartition data to spread the jobs more evenly throughout the cluster.

B.

High CPU time vs Task time means efficient use of cluster and no change needed

C.

High CPU time vs Task time means over-utilized memory and the need to increase parallelism

D.

High CPU time vs Task time means a CPU over-utilized job. The data engineer may need to consider executor and core tuning or resizing the cluster

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