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Answer: Partition the data that is in the S3 bucket. Organize the data by year, month, and day., Increase the AWS Glue instance size by scaling up the worker type.
Option A is CORRECT because partitioning the data in the S3 bucket by year, month, and day will help AWS Glue jobs process smaller, more manageable chunks of data. This can significantly reduce the time it takes to run the jobs and improve overall performance. Option B is CORRECT because increasing the AWS Glue instance size by scaling up the worker type can provide more computational resources, allowing the jobs to process data faster and handle larger datasets more efficiently.
Author: Ritesh Yadav
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Question 43/60
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.)
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.
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