Q: 9
In an AI data center, you are working with a professional administrator to optimize the deployment
of AI workloads across multiple servers. Which of the following actions would best contribute to
improving the efficiency and performance of the data center?
Options
Discussion
B not A
Yeah, it's A. Distributing AI jobs across GPU servers and using DPUs for network and storage just makes performance way better. Centralizing on one server (B) kills scalability. Pretty sure this matches NVIDIA's best practices but open to other views.
Probably A here. Spreading AI workloads across multiple GPU nodes with DPUs handling networking and storage is what NVIDIA's modern datacenter design pushes. That helps avoid bottlenecks and keeps both computation and IO efficient. B could overload a single server and C ignores the DPUs entirely, so I think A fits best. Happy for someone to point out if I'm missing a nuance.
My vote is A
I don’t think it’s B. A is much better since spreading out AI workloads across multiple GPU servers and offloading networking/storage tasks to DPUs really matches how NVIDIA builds for efficiency and scaling. Pretty sure that's the intent, but if you see a scenario with major hardware limits that'd matter.
C/D? Official guide explains distributed GPU setups with DPUs but practice test questions help clarify these details.
B is wrong, A fits better. Distributing loads and using DPUs for network/storage aligns with Nvidia's current best practices for AI data centers. Saw a similar scenario in a recent mock exam. Let me know if you disagree.
A makes more sense here. Splitting AI workloads across GPU servers and letting DPUs handle networking/storage boosts overall throughput and reduces CPU bottlenecks, especially for scalability. Pretty sure that matches how Nvidia recommends designing modern AI data centers.
Makes the most sense to split workloads across GPU servers and leverage DPUs, so A.
I don’t think A is right. B.
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