NCP-AIO Workload Management Practice Question
An MLOps engineer needs to guarantee that a latency-sensitive inference Deployment always has GPU capacity available, even when a large training Job is submitted to the same namespace. The cluster uses the NVIDIA GPU Operator and nodes have four A100 GPUs each. Which approach reliably reserves GPU capacity for the inference Deployment?
⚠ Common exam trap
The trap here is believing that a higher PriorityClass reserves GPU capacity, when it only enables eviction after a scheduling failure, not proactive reservation.
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
✓
Create a separate node pool and use nodeSelector or nodeAffinity to pin the inference Deployment to nodes tainted for inference only
Only a dedicated, tainted node pool combined with nodeSelector or nodeAffinity creates a hard capacity reservation that training workloads cannot violate. Priority-based preemption and MPS improve scheduling behavior or sharing but do not prevent a co-located training Job from consuming the GPUs first, and a namespace ResourceQuota limits aggregate requests without reserving specific capacity for the inference Deployment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a separate node pool and use nodeSelector or nodeAffinity to pin the inference Deployment to nodes tainted for inference only
Why this is correct
Dedicating a tainted node pool and pinning the inference Deployment with nodeSelector or nodeAffinity guarantees that training pods cannot consume those GPUs. Taints repel pods that lack the matching toleration, so the reserved capacity is genuinely protected regardless of how much training demand arrives, which is the only option that provides a hard reservation.
- ✗
Set a higher PriorityClass on the inference Deployment so it preempts training pods when needed
Why it's wrong here
Higher priority lets the inference pods preempt training pods, but preemption only triggers when the inference pod is pending and unschedulable. While the inference Deployment is running normally, it holds no reserved capacity, and a brief eviction and rescheduling delay occurs during preemption, so latency guarantees are weaker than a dedicated pool.
- ✗
Enable the NVIDIA MPS control daemon and configure each inference pod with a shared memory fraction
Why it's wrong here
MPS allows concurrent sharing of a single GPU among processes but does not reserve capacity or prevent a training pod from monopolizing a GPU. It improves utilization for small workloads, yet without a dedicated node pool or taints, a large training Job can still consume all available GPU memory and starve the inference service.
- ✗
Apply a ResourceQuota on the namespace that limits nvidia.com/gpu to the number of inference replicas
Why it's wrong here
A namespace ResourceQuota caps total GPU requests for all pods in that namespace. If training and inference share the namespace, the quota limits the combined usage but does not earmark any specific GPUs for inference, so a training Job could still consume the capacity first and block inference 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
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