NCP-AIO Troubleshooting and Optimization Practice Question
A multi-tenant NVIDIA GPU-accelerated Kubernetes cluster utilizing NVIDIA AI Enterprise experiences intermittent out-of-memory errors on Triton Inference Server pods despite adequate node memory reservation. Which monitoring and troubleshooting action correctly isolates the root cause?
⚠ Common exam trap
Many administrators mistakenly rely solely on standard cAdvisor memory metrics exposed by Kubernetes, completely missing GPU-specific memory exhaustion occurring inside the device driver context.
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
✓
Inspect DCGM-Exporter metrics for DCGM_FI_DEV_FB_FREE and DCGM_FI_DEV_GPU_TEMP alongside Triton server request concurrency logs.
Inspect NVIDIA Data Center GPU Manager metrics via Prometheus and DCGM-Exporter to capture real-time device memory consumption patterns. This practice is critical because standard Kubernetes container metrics fail to track internal GPU frame buffer allocations and fragmentation specific to deep learning inference engines.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Analyze cAdvisor container memory metrics using kubectl top pods to evaluate resident set size growth over sustained inference workloads.
Why it's wrong here
Standard cAdvisor metrics measure CPU memory consumption and do not account for GPU device memory allocated inside the NVIDIA driver. Relying on host container metrics will mask device-level frame buffer exhaustion happening directly on the accelerator.
- ✗
Review the Kubernetes cluster autoscaler logs to determine if node scaling events triggered unexpected pod evictions and restarts.
Why it's wrong here
Cluster autoscaler logs track node provisioning and scaling decisions rather than internal device memory allocation failures inside inference containers. Autoscaling activity does not explain why specific Triton instances ran out of accelerator frame buffer space.
- ✓
Inspect DCGM-Exporter metrics for DCGM_FI_DEV_FB_FREE and DCGM_FI_DEV_GPU_TEMP alongside Triton server request concurrency logs.
Why this is correct
Tracking free frame buffer metrics and GPU temperatures via DCGM-Exporter reveals exact memory headroom and potential throttling conditions during peak concurrency. Correlating these metrics with inference request logs confirms whether dynamic batching parameters exceeded available device memory.
- ✗
Execute systemctl status containerd on the worker node to verify container runtime stability and daemon responsiveness.
Why it's wrong here
Containerd daemon status reports runtime health, not per-pod GPU memory consumption, so it cannot reveal which process exhausted device memory. That command suits diagnosing container start failures or runtime crashes, not GPU allocation pressure inside Triton pods.
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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-AIO 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-AIO exam.