Answer-first summary for fast verification
Answer: Alter the Kinesis Data Firehose settings and Athena table schema to partition the data by date and time, and modify the Athena query to only access the necessary partitions.
The correct answer is D: 'Alter the Kinesis Data Firehose settings and Athena table schema to partition the data by date and time, and modify the Athena query to only access the necessary partitions.' Partitioning is a powerful technique to optimize query performance and reduce costs in Athena, especially for large and growing datasets. By partitioning the data by date and time, the query can be directed towards specific partitions, thereby processing only a relevant subset of the data, which significantly reduces the query time. This solution minimizes operational overhead as it leverages existing AWS capabilities without the need for custom scripts or additional management overhead.
Author: LeetQuiz Editorial Team
Ultimate access to all questions.
A company has set up a web application with AWS WAF logging to Amazon S3 via Amazon Kinesis Data Firehose. They have implemented a daily Amazon Athena query to analyze the previous day's AWS WAF logs, which have a consistent volume. Despite this, the query execution time has been increasing over time. A solutions architect is tasked with designing a solution to prevent this increase in query time while minimizing operational overhead. What is the most effective solution to achieve this goal?
A
Develop an AWS Lambda function to merge each day's AWS WAF logs into a single log file.
B
Configure AWS WAF to log data into a new S3 bucket daily to reduce the amount of data scanned.
C
Adjust the Kinesis Data Firehose settings to partition the S3 data by date and time, create external tables in Amazon Redshift, and use Amazon Redshift Spectrum to query the data.
D
Alter the Kinesis Data Firehose settings and Athena table schema to partition the data by date and time, and modify the Athena query to only access the necessary partitions.
No comments yet.