DEA-C01 · Question #141
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
The correct answer is C. Switch the task node type from general purpose Re instances to compute optimized EC2. Since the ETL job reaches maximum CPU usage but not memory usage, switching from general- purpose instances to compute-optimized instances (such as C5 or C6g instances) can provide better performance per dollar for CPU-bound workloads.
Question
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?
Options
- AIncrease the maximum number of task nodes for EMR managed scaling to 10.
- BChange the task node type from general purpose EC2 instances to memory optimized EC2
- CSwitch the task node type from general purpose Re instances to compute optimized EC2
- DReduce the scaling cooldown period for the provisioned EMR cluster.
How the community answered
(65 responses)- A11% (7)
- B2% (1)
- C82% (53)
- D6% (4)
Explanation
Since the ETL job reaches maximum CPU usage but not memory usage, switching from general- purpose instances to compute-optimized instances (such as C5 or C6g instances) can provide better performance per dollar for CPU-bound workloads.
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