Review set 3 question 47

[Jon984732]

The stem seems to be incomplete architecture or the answer is wrong. There is nothing in the stem indicating that the data source is 3 so whether the data is partitioned by day/month/year when stored in s3 after the glue job has no bearing on the performance of the job.

47. Question

An organization collects daily user interaction and app performance logs from its mobile app. These initially raw logs are transformed by AWS Glue for Spark Jobs and then stored in an Amazon S3 bucket for visualization with Amazon QuickSight which would make dynamic frames the second correct answer based on the question being asked.

The company’s Data Engineer has noticed a gradual slowdown in the QuickSight Dashboard’s query performance over time. Upon investigation, the engineer determined that the long duration of the AWS Glue Jobs is the root cause.

What combination of actions should the Data Engineer consider to improve query performance? (Select TWO.)

[Irene-TutorialsDojo]

Hi Jon984732,

Thanks for your feedback. We’ve reviewed and updated the question and explanation to clarify that the data in Amazon S3 is not partitioned. This helps explain why the AWS Glue jobs take longer and why partitioning the data is a relevant solution.

To briefly explain: partitioning the data by year, month, and day helps AWS Glue process only the necessary files instead of scanning everything, which speeds up the job. Using a larger worker type gives the job more compute resources, allowing it to finish faster.

These two actions help reduce the Glue job runtime, which improves the QuickSight dashboard performance.

Let us know if you have more suggestions!

Best,

Irene @ Tutorials Dojo