NVIDIA NCA-AIIO Exam - Topic 2 Question 5 Discussion

Actual exam question for NVIDIA's NCA-AIIO exam

Question #: 5
Topic #: 2

In your multi-tenant AI cluster, multiple workloads are running concurrently, leading to some jobs experiencing performance degradation. Which GPU monitoring metric is most critical for identifying resource contention between jobs?

AGPU Utilization Across Jobs

BGPU Temperature

CNetwork Latency

DMemory Bandwidth Utilization

Show Suggested Answer

Suggested Answer: A

GPU Utilization Across Jobs is the most critical metric for identifying resource contention in a multi-tenant cluster. It shows how GPU resources are divided among workloads, revealing overuse or starvation via tools like nvidia-smi. Option B (temperature) indicates thermal issues, not contention. Option C (network latency) affects distributed tasks. Option D (memory bandwidth) is secondary. NVIDIA's DCGM supports this metric for contention analysis.

by Pamella at Jun 12, 2025, 12:33 AM

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Sanjuana

8 months ago

Hmm, I thought Network Latency would be more relevant.

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Annamae

8 months ago

Totally agree, GPU Utilization is where it's at!

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Jamal

8 months ago

I think Memory Bandwidth Utilization is also super important.

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France

9 months ago

Wait, are we really saying GPU Temperature matters here?

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Tammara

9 months ago

Definitely GPU Utilization Across Jobs! That's the key metric.

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Lenna

9 months ago

I’m leaning towards GPU Utilization as well, but I wonder if GPU Temperature could indicate throttling issues that might affect performance too.

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Helaine

9 months ago

I feel like we discussed network latency in a similar question, but it seems less related to resource contention compared to GPU utilization.

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Eleonora

9 months ago

I'm not entirely sure, but I remember something about Memory Bandwidth Utilization being important for performance issues. Could that be relevant?

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Meaghan

10 months ago

I think GPU Utilization Across Jobs might be the key metric here since it directly reflects how much of the GPU's resources are being used by each job.

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Verda

10 months ago

The GPU utilization metric seems like the most logical choice here. If we're seeing uneven utilization across the GPUs, that could be a sign that the jobs are competing for resources and causing performance degradation.

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Aliza

10 months ago

I'm a little confused by this question. I'm not sure which metric would be most critical for identifying resource contention. Maybe I should review my notes on GPU monitoring and resource management.

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Stephaine

10 months ago

Okay, I think the key here is to look at the GPU utilization metric. If we're seeing high utilization on some GPUs while others are underutilized, that could indicate contention between the jobs.

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Laquanda

10 months ago

Hmm, I'm a bit unsure about this one. I'm trying to think through the different metrics and how they might relate to the problem. GPU temperature and network latency don't seem as directly relevant.

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Josphine

11 months ago

This seems like a pretty straightforward question. I'd focus on GPU utilization across the different jobs to identify resource contention.

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Margret

1 year ago

GPU Temperature, huh? That's a good one. Maybe we can just put a bunch of fans in the server room and call it a day. Where's the fun in that?

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Leah

1 year ago

User 3: Network Latency could also play a role in performance degradation.

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Laticia

1 year ago

User 2: I think Memory Bandwidth Utilization is also important to consider.

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Carissa

1 year ago

User 1: GPU Utilization Across Jobs is more critical for identifying resource contention.

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Reena

1 year ago

Network Latency? Really? Unless your jobs are all the way across the cluster, I don't see how that's going to help you identify resource contention. GPU Utilization is the way to go, folks.

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