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Answer: Switch the task node type from general purpose Re instances to compute optimized EC2 instances.
Option C is CORRECT because switching the task node type from general-purpose EC2 instances to compute-optimized EC2 instances is the most cost-effective solution given the scenario. Since the EMR cluster often reaches maximum CPU usage but has low memory usage, using compute-optimized instances is more suitable. Compute-optimized instances provide more CPU resources relative to memory, allowing the cluster to handle the CPU-intensive nature of the ETL job without overprovisioning memory, which helps in reducing costs.
Author: Ritesh Yadav
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Question 23/58
A company currently uses a provisioned Amazon EMR cluster that includes general purpose Amazon EC2 instances. The EMR cluster uses EMR managed scaling between one to five task nodes for the company’s long-running Apache Spark extract, transform, and load (ETL) job. The company runs the ETL job every day.
When the company runs the ETL job, the EMR cluster quickly scales up to five nodes. The EMR cluster often reaches maximum CPU usage, but the memory usage remains under 30%.
The company wants to modify the EMR cluster configuration to reduce the EMR costs to run the daily ETL job.
Which solution will meet these requirements MOST cost-effectively?
A
Increase the maximum number of task nodes for EMR managed scaling to 10.
B
Change the task node type from general purpose EC2 instances to memory optimized EC2 instances.
C
Switch the task node type from general purpose Re instances to compute optimized EC2 instances.
D
Reduce the scaling cooldown period for the provisioned EMR cluster.
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