NCP-AIO Workload Management Practice Question
An AI operations team manages a shared Kubernetes cluster where a nightly batch training workload requests nvidia.com/gpu resources and occasionally consumes all GPU memory on a node, causing a co-located interactive notebook pod to fail with CUDA out-of-memory errors. The team wants the interactive notebook to be isolated from the batch workload's memory usage without adding new hardware. Which action best achieves this on supported data center GPUs?
⚠ Common exam trap
The trap here is assuming that time-slicing or container memory limits isolate GPU memory, when neither partitions the device framebuffer and only MIG provides hardware-level memory separation.
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
✓
Configure Multi-Instance GPU profiles so the notebook and batch workloads run on separate MIG instances with dedicated memory partitions on the same physical GPU.
The conflict is shared GPU memory, not scheduling order. MIG is the only listed mechanism that gives each workload a dedicated, hardware-isolated memory partition on the same physical device, letting the notebook and batch job coexist without the batch job starving the notebook's framebuffer, and it requires no extra hardware.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable time-slicing with a replica count of four so the notebook and batch workloads alternate on the same GPU in round-robin fashion.
Why it's wrong here
Time-slicing interleaves execution but does not partition memory, so all containers still share the full framebuffer. If the batch workload allocates heavily, it can still exhaust memory and cause the notebook to fail, which means this approach does not deliver the isolation the team needs.
- ✗
Set a memory limit on the batch container using the standard Kubernetes resources.limits.memory field to cap its GPU framebuffer usage.
Why it's wrong here
The resources.limits.memory field governs host system memory, not GPU device memory. Applying it would restrict the container's RAM and could trigger host-level OOM kills, but it has no effect on how much framebuffer the CUDA workload allocates, so the notebook would remain vulnerable.
- ✓
Configure Multi-Instance GPU profiles so the notebook and batch workloads run on separate MIG instances with dedicated memory partitions on the same physical GPU.
Why this is correct
MIG slices a supported GPU into hardware-isolated instances, each with its own dedicated memory and compute resources. Placing the notebook and batch workload on separate instances prevents the batch job from consuming the notebook's memory, resolving the out-of-memory failures without procuring additional GPUs.
- ✗
Add a higher PriorityClass to the notebook pod so the kube-scheduler preempts the batch workload whenever memory pressure occurs.
Why it's wrong here
PriorityClass affects scheduling and preemption decisions at admission time; it does not react to runtime GPU memory pressure or evict a running workload because of an allocation failure. The batch pod would keep its framebuffer, and the notebook would still crash, so priority cannot substitute for memory isolation.
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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.