DEA-C01 · Question #16
A company uses an Amazon QuickSight dashboard to monitor usage of one of the company's applications. The company uses AWS Glue jobs to process data for the dashboard. The company stores the data in a
The correct answer is A. Partition the data that is in the S3 bucket. Organize the data by year, month, and day. B. Increase the AWS Glue instance size by scaling up the worker type.. To improve the performance of long-running AWS Glue jobs that feed an Amazon QuickSight dashboard, the data in the Amazon S3 bucket should be partitioned by date (year, month, and day) to reduce the data scanned, and the AWS Glue job's worker type or count should be increased to
Question
A company uses an Amazon QuickSight dashboard to monitor usage of one of the company's applications. The company uses AWS Glue jobs to process data for the dashboard. The company stores the data in a single Amazon S3 bucket. The company adds new data every day. A data engineer discovers that dashboard queries are becoming slower over time. The data engineer determines that the root cause of the slowing queries is long-running AWS Glue jobs. Which actions should the data engineer take to improve the performance of the AWS Glue jobs? (Choose two.)
Options
- APartition the data that is in the S3 bucket. Organize the data by year, month, and day.
- BIncrease the AWS Glue instance size by scaling up the worker type.
- CConvert the AWS Glue schema to the DynamicFrame schema class.
- DAdjust AWS Glue job scheduling frequency so the jobs run half as many times each day.
- EModify the IAM role that grants access to AWS glue to grant access to all S3 features.
How the community answered
(15 responses)- A73% (11)
- C13% (2)
- D7% (1)
- E7% (1)
Why each option
To improve the performance of long-running AWS Glue jobs that feed an Amazon QuickSight dashboard, the data in the Amazon S3 bucket should be partitioned by date (year, month, and day) to reduce the data scanned, and the AWS Glue job's worker type or count should be increased to provide more compute resources.
Partitioning data in Amazon S3, especially by date when new data is added daily, significantly reduces the amount of data that AWS Glue jobs and subsequent QuickSight queries need to scan, directly improving job and query performance.
Increasing the AWS Glue job's instance size (worker type) or number of workers provides more compute resources (CPU, memory), allowing the job to process data faster and complete in less time.
Converting to a DynamicFrame schema class is part of how AWS Glue handles data but does not inherently provide a direct performance improvement for long-running jobs; it's more about data manipulation and schema management within Glue.
Adjusting the AWS Glue job scheduling frequency will not improve the performance of individual job runs and might even lead to processing more data per run, potentially worsening the problem.
Modifying the IAM role to grant more S3 features will not improve AWS Glue job performance unless the job was previously failing due to insufficient permissions, which is not indicated as the root cause.
Concept tested: AWS Glue job performance optimization
Source: https://docs.aws.amazon.com/glue/latest/dg/partition-best-practices.html
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