NVIDIA NCA-AIIO Exam - Topic 3 Question 6 Discussion

Actual exam question for NVIDIA's NCA-AIIO exam

Question #: 6
Topic #: 3

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?

AThe AI model is not compatible with multi-GPU training.

BThe GPUs are not properly connected in the cluster.

CIncorrect software version installed on the GPUs.

DThe data preprocessing is being bottlenecked by the CPU.

Show Suggested 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

4 months ago

High CPU usage usually means data preprocessing is the problem.

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Adelaide

4 months ago

Wait, could it be the model compatibility issue?

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Tomas

4 months ago

I doubt it's the connection; that seems unlikely.

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Aja

4 months ago

Definitely, D is the most likely cause.

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Edison

5 months ago

Sounds like a CPU bottleneck to me.

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Pamela

5 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

5 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

5 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

5 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

6 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

6 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

6 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

6 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

9 months 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

9 months 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

9 months 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

9 months ago

C) Incorrect software version installed on the GPUs.

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Paris

9 months ago

B) The GPUs are not properly connected in the cluster.

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Yen

9 months ago

A) The AI model is not compatible with multi-GPU training.

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Jackie

10 months 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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