NCP-AIO Workload Management Practice Question
Network Topology
Refer to the exhibit. The cluster administrator observes near-capacity memory utilization across three GPUs. What is the most likely consequence if an additional pod is scheduled to these GPUs without memory partitioning?
⚠ Common exam trap
Candidates might guess that the system will simply slow down or swap to system RAM, forgetting that GPU memory is non-swappable and will cause immediate process termination via OOM error.
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
✓
The job will experience OOM errors and fail.
Scheduling an additional pod onto these GPUs will lead to Out-of-Memory (OOM) errors. Because the GPUs are already at near-maximum capacity, the driver will likely kill the incoming process or potentially destabilize existing tasks. This highlights the importance of workload monitoring and resource planning, as AI Ops must ensure that requests match actual hardware capacity to prevent catastrophic job failures 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.
- ✗
The scheduler will automatically enable MIG.
Why it's wrong here
The Kubernetes scheduler is not capable of dynamically reconfiguring GPU hardware profiles like MIG at runtime. Enabling MIG requires system-level changes to the GPU configuration, which must be performed by the cluster administrator or an automated operator before the node can accept workloads formatted for those specific instances.
- ✓
The job will experience OOM errors and fail.
Why this is correct
With memory utilization already exceeding 95% on all GPUs, there is insufficient headroom to spawn a new process. The operating system or the NVIDIA driver will trigger an Out-of-Memory (OOM) kill signal, resulting in the immediate failure of the new container and potential instability for existing tasks.
- ✗
The scheduler will use CPU swap memory.
Why it's wrong here
GPU memory is distinct from system RAM. NVIDIA GPUs do not use CPU swap memory for their internal frame buffer or memory allocations. If the memory footprint of the workload exceeds the physical memory available on the GPU, the allocation request will fail at the driver level, crashing the application.
- ✗
The job will be throttled but complete successfully.
Why it's wrong here
GPU memory is a finite hardware resource that cannot be over-subscribed in the same way as CPU cycles. There is no mechanism to 'throttle' memory allocation while allowing a process to continue; if the requested memory is not available at allocation time, the process will crash immediately.
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-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.