DEA-C01 Question 131
Select 2You are working as a data engineer for a company that processes large datasets daily. The company uses Amazon EMR to run distributed computing jobs using Apache Spark. During a recent job execution, you noticed that the job is taking significantly longer than expected, and the cluster is underutilized. Which of the following actions should you take to optimize the performance of your distributed computing job?
- A
Increase the number of partitions for your dataset to improve parallelism.
- B
Use the default instance type for your EMR cluster without changing the configuration.
- C
Enable Dynamic Allocation in Apache Spark to optimize resource utilization.
- D
Use the coalesce() function to reduce the number of partitions to 1.
- E
Provision a larger EMR cluster with more powerful instance types.
Show answer and explanation
Correct answers: A, C
Explanation
In distributed computing, optimizing resource utilization and parallelism are key to improving performance. Increasing the number of partitions ensures that the workload is distributed across more tasks, allowing better utilization of the cluster. Dynamic Allocation in Apache Spark further helps by automatically scaling resources to match the workload. Other options either reduce parallelism, fail to address the root cause, or increase costs unnecessarily.
- A. Correct.
Increasing the number of partitions allows the workload to be distributed across more tasks, which can improve parallelism and reduce job execution time.
- B. Incorrect.
Using the default instance type without adjusting the configuration may result in suboptimal performance, especially for compute-intensive jobs.
- C. Correct.
Enabling Dynamic Allocation in Apache Spark helps to optimize resource utilization by dynamically adjusting the number of executors based on workload demands.
- D. Incorrect.
Using the coalesce() function to reduce the number of partitions to 1 would reduce parallelism and likely increase job execution time.
- E. Incorrect.
Provisioning a larger EMR cluster with more powerful instance types may not address the root cause of underutilization and could lead to unnecessary costs.