NCP-GENL Production Monitoring and Reliability Practice Question
An LLM application deployed on NVIDIA Triton Inference Server is experiencing intermittent latency spikes. Which metric is most critical to monitor to determine if the issue stems from GPU compute saturation?
⚠ Common exam trap
Candidates often monitor general host CPU utilization or basic GPU memory usage, failing to recognize that SM occupancy directly reflects compute execution core saturation.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
SM (Streaming Multiprocessor) occupancy
Monitoring GPU utilization alone is insufficient because it does not distinguish between compute saturation and memory bandwidth bottlenecks. NVML-based metrics specifically tracking SM (Streaming Multiprocessor) occupancy provide the granular insight required to identify whether the execution cores are fully utilized. This metric is essential for capacity planning and ensuring that the inference throughput meets strict service level agreements under high concurrent request loads in production environments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Total system CPU usage
Why it's wrong here
CPU usage monitors general processing tasks but fails to capture the internal state of the GPU's compute units. Since inference workloads primarily execute on the GPU, CPU metrics are often misleading or irrelevant for diagnosing GPU-side compute saturation during high-demand periods.
- ✗
Network interface throughput
Why it's wrong here
Network throughput measures the volume of data moving into and out of the server. While high network latency can cause perceived delays, it does not provide evidence of compute saturation on the GPU itself, making it an ineffective indicator for model execution performance.
- ✓
SM (Streaming Multiprocessor) occupancy
Why this is correct
SM occupancy directly measures the percentage of active warps compared to the maximum supported by the GPU. High occupancy indicates that the streaming multiprocessors are busy executing instructions, serving as the primary metric for identifying compute-bound bottlenecks in neural network inference tasks.
- ✗
Total GPU memory capacity
Why it's wrong here
Memory capacity tracks whether the VRAM is full, which leads to out-of-memory errors rather than latency spikes. If memory is sufficient, monitoring total capacity provides no information regarding the efficiency or saturation of the compute engines during the model inference phase.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
This NCP-GENL practice question is part of Courseiva's free NVIDIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the NCP-GENL exam.