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Question 412 - SAP-C02 discussion

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A company deploys workloads in multiple AWS accounts. Each account has a VPC with VPC flow logs published in text log format to a centralized Amazon S3 bucket. Each log file is compressed with gzjp compression. The company must retain the log files indefinitely.

A security engineer occasionally analyzes the togs by using Amazon Athena to query the VPC flow logs. The query performance is degrading over time as the number of ingested togs is growing. A solutions architect: must improve the performance of the tog analysis and reduce the storage space that the VPC flow logs use.

Which solution will meet these requirements with the LARGEST performance improvement?

A.
Create an AWS Lambda function to decompress the gzip flies and to compress the tiles with bzip2 compression. Subscribe the Lambda function to an s3: ObiectCrealed;Put S3 event notification for the S3 bucket.
Answers
A.
Create an AWS Lambda function to decompress the gzip flies and to compress the tiles with bzip2 compression. Subscribe the Lambda function to an s3: ObiectCrealed;Put S3 event notification for the S3 bucket.
B.
Enable S3 Transfer Acceleration for the S3 bucket. Create an S3 Lifecycle configuration to move files to the S3 Intelligent-Tiering storage class as soon as the ties are uploaded
Answers
B.
Enable S3 Transfer Acceleration for the S3 bucket. Create an S3 Lifecycle configuration to move files to the S3 Intelligent-Tiering storage class as soon as the ties are uploaded
C.
Update the VPC flow log configuration to store the files in Apache Parquet format. Specify Hourly partitions for the log files.
Answers
C.
Update the VPC flow log configuration to store the files in Apache Parquet format. Specify Hourly partitions for the log files.
D.
Create a new Athena workgroup without data usage control limits. Use Athena engine version 2.
Answers
D.
Create a new Athena workgroup without data usage control limits. Use Athena engine version 2.
Suggested answer: C

Explanation:

Converting VPC flow logs to store in Apache Parquet format and specifying hourly partitions significantly improves query performance and reduces storage space usage. Apache Parquet is a columnar storage file format optimized for analytical queries, allowing Athena to scan less data and improve query performance. Partitioning logs by hour further enhances query efficiency by limiting the amount of data scanned during queries, addressing the issue of degrading performance over time due to the growing volume of ingested logs.

asked 16/09/2024
Felipe Santos Cardoso
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