A company uses an Amazon QuickSight dashboard fed by AWS Glue jobs that process data stored in a single S3 bucket. New data is added daily, and dashboard queries are slowing because the AWS Glue jobs are running longer. Which actions should the data engineer take to speed up the AWS Glue jobs? (Choose two.)
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Correct 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..
Why this is the answer
Partitioning data in S3 by year, month, and day significantly improves AWS Glue job performance. When data is partitioned, Glue jobs can leverage predicate pushdown, meaning they only read the specific data relevant to a query, rather than scanning the entire dataset. This drastically reduces the amount of data processed, leading to faster job execution. Increasing the AWS Glue instance size (scaling up the worker type) provides more computational resources (CPU and memory) to the job, allowing it to process data faster. Converting to DynamicFrame schema is not directly related to job speed. Adjusting scheduling frequency doesn't speed up individual jobs, it just runs them less often. Modifying the IAM role for all S3 features is a security concern and doesn't impact job performance.
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