In a DNA decoding project using cloud resources, which resource is MOST important for the university to request from the provider?

In a DNA decoding project using cloud resources, which resource is MOST important for the university to request from the provider?

• A. Large storage for the DNA decoding results
• B. A larger pipe to transfer the results
• C. A closer datacenter to the university
• D. Any available compute resource

Answer: (C)

When handling large biological datasets in the cloud, how fast you can move data between the cloud and your campus often sets the pace for the whole workflow. Latency and transfer speed between the university and the cloud provider directly impact how quickly results are obtained and how smoothly iterative analyses run. Requesting a datacenter that is physically close to the university minimizes network round-trip times, improves data transfer performance, and can reduce data egress costs. This proximity effectively makes compute and analysis feel local, which is especially important when decoding large DNA datasets where you frequently move big files in and out of the cloud. While having ample storage or a bigger network pipe helps, they don’t address the fundamental bottleneck of moving large volumes of data over long distances. A closer datacenter provides the most impactful improvement in speed and responsiveness for this workload. Even with access to compute resources elsewhere, being near the university’s location ensures faster access to data and quicker delivery of results.

When handling large biological datasets in the cloud, how fast you can move data between the cloud and your campus often sets the pace for the whole workflow. Latency and transfer speed between the university and the cloud provider directly impact how quickly results are obtained and how smoothly iterative analyses run. Requesting a datacenter that is physically close to the university minimizes network round-trip times, improves data transfer performance, and can reduce data egress costs. This proximity effectively makes compute and analysis feel local, which is especially important when decoding large DNA datasets where you frequently move big files in and out of the cloud.

While having ample storage or a bigger network pipe helps, they don’t address the fundamental bottleneck of moving large volumes of data over long distances. A closer datacenter provides the most impactful improvement in speed and responsiveness for this workload. Even with access to compute resources elsewhere, being near the university’s location ensures faster access to data and quicker delivery of results.