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NVIDIA NCA-AIIO Exam - Topic 3 Question 6 Discussion

You have deployed an AI training job on a GPU cluster, but the training time has not decreased as expected after adding more GPUs. Upon further investigation, you observe that the GPU utilization is low, and the CPU utilization is very high. What is the most likely cause of this issue?
D) The data preprocessing is being bottlenecked by the CPU.
A) The AI model is not compatible with multi-GPU training.
B) The GPUs are not properly connected in the cluster.
C) Incorrect software version installed on the GPUs.

NVIDIA NCA-AIIO Exam - Topic 3 Question 6 Discussion

Actual exam question for NVIDIA's NCA-AIIO exam
Question #: 6
Topic #: 3
[All NCA-AIIO Questions]

You have deployed an AI training job on a GPU cluster, but the training time has not decreased as expected after adding more GPUs. Upon further investigation, you observe that the GPU utilization is low, and the CPU utilization is very high. What is the most likely cause of this issue?

Show Suggested Answer Hide Answer
Suggested Answer: D

The data preprocessing being bottlenecked by the CPU is the most likely cause. High CPU utilization and low GPU utilization suggest the GPUs are idle, waiting for data, a common issue when preprocessing (e.g., data loading) is CPU-bound. NVIDIA recommends GPU-accelerated preprocessing (e.g., DALI) to mitigate this. Option A (model incompatibility) would show errors, not low utilization. Option B (connection issues) would disrupt communication, not CPU load. Option C (software version) is less likely without specific errors. NVIDIA's performance guides highlight preprocessing bottlenecks.


by Eve at Jun 11, 2025, 05:33 AM

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Jesusita
7 months ago
High CPU usage usually means data preprocessing is the problem.
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Adelaide
7 months ago
Wait, could it be the model compatibility issue?
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Tomas
7 months ago
I doubt it's the connection; that seems unlikely.
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Aja
7 months ago
Definitely, D is the most likely cause.
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Edison
7 months ago
Sounds like a CPU bottleneck to me.
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Pamela
8 months ago
I wonder if the GPUs not being connected properly could cause this, but it seems less likely than the CPU bottleneck.
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Albina
8 months ago
I feel like I've seen a similar question before, and it was about the CPU handling too much work while the GPUs were waiting. Could that be what's happening?
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Oneida
8 months ago
I'm not entirely sure, but I think if the model isn't set up for multi-GPU training, it could lead to low GPU utilization.
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Lindsey
8 months ago
I remember reading about how data preprocessing can often become a bottleneck, especially if the CPU is overloaded. That might be the issue here.
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Shizue
9 months ago
I'm a bit unsure about this one. The GPU utilization being low is the key clue, but I'm not totally confident in ruling out the other options just yet. I'll need to think it through carefully.
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Malinda
9 months ago
Alright, this is a good one. I'm leaning towards option D - the data preprocessing being the bottleneck. Gotta make sure I understand the details though before committing to that.
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Gerald
9 months ago
Okay, let's see here. The low GPU utilization and high CPU usage makes me think it's probably the data preprocessing step. I'll mark that as my answer for now.
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Justine
9 months ago
Hmm, this seems like a tricky one. I'm thinking it could be the data preprocessing bottleneck, but I'll need to carefully consider the other options as well.
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Fannie
1 year ago
Option D is the way to go. Clearly, the CPU is the weakest link in the chain, and that's slowing down the whole process. Time to get a faster CPU!
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Natalie
1 year ago
Haha, I bet the person who wrote this question is a CPU enthusiast trying to trick us. But D is definitely the correct answer.
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Tanja
1 year ago
I agree, D is the right answer. Optimization is key in AI training, and the CPU is often the culprit when GPU utilization is low.
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Denae
12 months ago
C) Incorrect software version installed on the GPUs.
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Paris
12 months ago
B) The GPUs are not properly connected in the cluster.
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Yen
12 months ago
A) The AI model is not compatible with multi-GPU training.
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Jackie
1 year ago
The most likely cause is D) The data preprocessing is being bottlenecked by the CPU. Makes sense, if the CPU is struggling, it won't be able to feed the GPUs fast enough.
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Erick
12 months ago
Corazon: It sounds like the data preprocessing is being bottlenecked by the CPU.
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Corazon
1 year ago
User 2: Yes, the GPU utilization is low and the CPU utilization is very high.
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Chun
1 year ago
User 1: Have you checked the GPU and CPU utilization?
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Loise
1 year ago
But could it also be due to incorrect software version on the GPUs?
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Margurite
1 year ago
I agree with Tamala, high CPU utilization could be bottlenecking the data preprocessing.
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Tamala
1 year ago
I think the issue might be with the data preprocessing.
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