AWS Certified CloudOps Engineer Associate SOA-C03 Practice Question
A data analytics team uploads 6 TB of logs nightly from its on-premises NFS server to a single Amazon S3 bucket. The job runs on a Linux EC2 instance over a 10 Gbps AWS Direct Connect link, using the AWS CLI s3 cp command. Runtime is now 14 hours, delaying downstream AWS Glue processing. Without adding EC2 instances or changing the link, which action will most quickly and substantially reduce transfer time with minimal effort?
Rewrite the copy script to use the AWS SDK Amazon S3 Transfer Manager for multipart, concurrent uploads.
Create an AWS DataSync task that copies the files from the on-premises NFS server directly to the S3 bucket.
Enable S3 Transfer Acceleration on the bucket and rerun the nightly upload through the accelerate endpoint.
Set s3.max_concurrent_requests to 20 in the AWS CLI profile before running the existing s3 cp command.
AWS DataSync is a managed data-transfer service that automatically performs highly parallel, multipart uploads with inline compression. One task can saturate a 10 Gbps link, so copying 6 TB can finish in roughly 2 hours instead of 14, without tuning the EC2 host. Creating an agent VM and a scheduled task takes only minutes and requires no code maintenance, so operational overhead remains low.
S3 Transfer Acceleration optimizes internet paths and provides little or no benefit when traffic already flows over a private Direct Connect circuit. Rewriting the script to use the AWS SDK Transfer Manager, or merely increasing the AWS CLI s3.max_concurrent_requests setting, could improve performance but still relies on the EC2 host and requires script or configuration changes. Neither is likely to match DataSync's throughput or ease of use.
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What is AWS DataSync and how does it improve data transfers?
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How does S3 Transfer Acceleration differ from AWS Direct Connect?
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Why doesn't adjusting AWS CLI settings like s3.max_concurrent_requests match DataSync's performance?
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AWS Certified CloudOps Engineer Associate SOA-C03
Monitoring, Logging, Analysis, Remediation, and Performance Optimization
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