Topic 1 Question 85
An online retail company stores Application Load Balancer (ALB) access logs in an Amazon S3 bucket. The company wants to use Amazon Athena to query the logs to analyze traffic patterns.
A data engineer creates an unpartitioned table in Athena. As the amount of the data gradually increases, the response time for queries also increases. The data engineer wants to improve the query performance in Athena.
Which solution will meet these requirements with the LEAST operational effort?
Create an AWS Glue job that determines the schema of all ALB access logs and writes the partition metadata to AWS Glue Data Catalog.
Create an AWS Glue crawler that includes a classifier that determines the schema of all ALB access logs and writes the partition metadata to AWS Glue Data Catalog.
Create an AWS Lambda function to transform all ALB access logs. Save the results to Amazon S3 in Apache Parquet format. Partition the metadata. Use Athena to query the transformed data.
Use Apache Hive to create bucketed tables. Use an AWS Lambda function to transform all ALB access logs.
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- 正解だと思う選択肢: B
Creating an AWS Glue crawler (Option B) is the most straightforward and least operationally intensive approach to automatically determine the schema, partition the data, and keep the AWS Glue Data Catalog updated. This ensures Athena queries are optimized without requiring extensive manual management or additional processing steps.
👍 5PGGuy2024/06/21 - 正解だと思う選択肢: B
An AWS Glue crawler can automatically determine the schema of the logs, infer partitions, and update the Glue Data Catalog. Crawlers can be scheduled to run at intervals, minimizing manual intervention.
👍 4tgv2024/06/15 Creating an AWS Glue crawler (Option B) is the most straightforward and least operationally intensive approach to automatically determine the schema, partition the data, and keep the AWS Glue Data Catalog updated. This ensures Athena queries are optimized without requiring extensive manual management or additional processing steps.
👍 2PGGuy2024/06/21
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