Exam NCP-AIO Topic 2 Question 6 Discussion

Actual exam question for NVIDIA's NCP-AIO exam
Question #: 6
Topic #: 2

A data scientist reports that a Run.ai job is consistently crashing with a 'SIGKILL' signal. After verifying that the job is not exceeding its resource limits (CPU, memory, GPU), what is the MOST likely reason for this signal, and how can you diagnose it further within the Run.ai environment?

A. The job is experiencing a kernel panic on the node. Check the system logs on the node for kernel-related errors. B. The Kubernetes liveness probe is failing, causing the pod to be restarted. Inspect the pod's events and liveness probe configuration. C. The Run.ai agent is terminating the job due to exceeding a pre-defined time limit. Check the job's configuration for any time limits or deadlines. D. The job is being preempted due to higher priority tasks, resulting in SIGKILL. Check the job's priority, quota, and resource usage history with ACM. E. The process is consuming a lot of disk I/O.

Suggested Answer: B Vote an answer

A 'SIGKILL' signal often indicates that the process was forcibly terminated by the operating system or a container runtime. A failing Kubernetes liveness probe is a common cause. If the probe fails, Kubernetes will restart the pod, sending a SIGKILL to the existing process. You can diagnose this by inspecting the pod's events using 'kubectl describe pod or 'runai describe job and examining the liveness probe configuration in the pod's YAML definition. Kernel panics, Run.ai agent time limits, and preemption are less likely to result directly in a SIGKILL signal.

by Mark at Mar 18, 2026, 04:44 AM

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