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NVIDIA NCA-AIIO Exam - Topic 2 Question 5 Discussion

Actual exam question for NVIDIA's NCA-AIIO exam
Question #: 5
Topic #: 2
[All NCA-AIIO Questions]

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?

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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
4 months ago
Hmm, I thought Network Latency would be more relevant.
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Annamae
4 months ago
Totally agree, GPU Utilization is where it's at!
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Jamal
4 months ago
I think Memory Bandwidth Utilization is also super important.
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France
4 months ago
Wait, are we really saying GPU Temperature matters here?
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Tammara
4 months ago
Definitely GPU Utilization Across Jobs! That's the key metric.
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Lenna
5 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
5 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
5 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
5 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
5 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
6 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
6 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
6 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
6 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
9 months 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
8 months ago
User 3: Network Latency could also play a role in performance degradation.
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Laticia
8 months ago
User 2: I think Memory Bandwidth Utilization is also important to consider.
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Carissa
8 months ago
User 1: GPU Utilization Across Jobs is more critical for identifying resource contention.
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Reena
9 months 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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Tanja
9 months ago
I'm going with D, Memory Bandwidth Utilization. Gotta keep an eye on that memory pipeline, you know? Can't have jobs hogging all the bandwidth.
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Queenie
8 months ago
User 3: I see your point, but I still think D) Memory Bandwidth Utilization is the most critical. We can't overlook the memory pipeline.
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Lovetta
8 months ago
User 2: I agree with Lovetta, GPU utilization is key to identifying resource contention.
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Joni
9 months ago
User 1: I think A) GPU Utilization Across Jobs is more critical. We need to see how much each job is using the GPU.
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Angelica
10 months ago
I agree, GPU Utilization is the way to go. You can't optimize what you can't measure, am I right?
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Corrinne
9 months ago
I agree, GPU Utilization is the way to go. You can't optimize what you can't measure, am I right?
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Denae
9 months ago
A) GPU Utilization Across Jobs
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Selma
10 months ago
But what about Memory Bandwidth Utilization? That could also be important for identifying contention.
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Lucia
10 months ago
I agree with Celia, high GPU utilization across jobs can indicate resource contention.
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Celia
10 months ago
I think the most critical metric is GPU Utilization Across Jobs.
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Deandrea
10 months ago
GPU Utilization Across Jobs is definitely the key metric to look at. That's where the resource contention will show up first.
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Izetta
9 months ago
D) Memory Bandwidth Utilization could be another important metric to monitor.
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Georgeanna
9 months ago
B) GPU Temperature may also play a role in performance degradation.
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Sherell
10 months ago
A) GPU Utilization Across Jobs is crucial for identifying resource contention.
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