AWS Certified Data Engineer – Associate (DEA-C01) — Question 63

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.)

Answer options

• 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.
• C. Convert the AWS Glue schema to the DynamicFrame schema class.
• D. Adjust AWS Glue job scheduling frequency so the jobs run half as many times each day.
• E. Modify the IAM role that grants access to AWS glue to grant access to all S3 features.

Correct answer: A, B

Explanation

Partitioning the data in the S3 bucket by year, month, and day can significantly improve query performance by allowing AWS Glue to read only the relevant portions of data. Increasing the AWS Glue instance size by scaling up the worker type can also enhance performance by providing more resources for processing. The other options do not directly address the performance issues related to long-running jobs.