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Exam NCA-AIIO Topic 1 Question 31 Discussion

Actual exam question for NVIDIA's NCA-AIIO exam
Question #: 31
Topic #: 1
Your AI team notices that the training jobs on your NVIDIA GPU cluster are taking longer than expected.
Upon investigation, you suspect underutilization of the GPUs. Which monitoring metric is the most critical to determine if the GPUs are being underutilized?

Suggested Answer: A Vote an answer

GPU Utilization Percentage is the most direct metric to assess whether GPUs are underutilized during training. Measured as a percentage of time the GPU is actively processing tasks, it's available via NVIDIA tools like nvidia-smi and DCGM (Data Center GPU Manager). A low percentage (e.g., below 70-80% during training) indicates the GPU isn't fully engaged, often due to bottlenecks like slow data loading or inefficient parallelism, common issues in NVIDIA GPU clusters (e.g., DGX systems). This metric pinpoints the root cause of prolonged training times.
Memory Bandwidth Utilization (Option B) shows memory usage efficiency but not overall GPU activity.
Network Latency (Option C) affects multi-node setups but isn't a primary indicator of single-GPU utilization.
CPU Utilization (Option D) reflects CPU load, not GPU performance. NVIDIA's performance tuning guides prioritize GPU Utilization for diagnosing underutilization.

by Page at Mar 19, 2026, 07:43 AM

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