NCP-AIO Workload Management Practice Question
A platform team operates a Kubernetes cluster where several teams submit GPU training jobs. The administrator needs to enforce per-namespace limits on the number of GPUs that can be consumed and prevent a single namespace from monopolizing all GPU capacity. Which TWO Kubernetes resources should be configured to achieve this? (Choose two.)
⚠ Common exam trap
The trap here is believing that PriorityClass or NetworkPolicy can cap GPU consumption, when only quota and limit-range objects act on resource quantities at admission time.
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
✓
A LimitRange that sets a default and maximum nvidia.com/gpu value for containers in the namespace.
ResourceQuota enforces an aggregate ceiling on nvidia.com/gpu per namespace, while LimitRange constrains and defaults the per-container GPU request. Together they bound total namespace consumption and prevent any single container from grabbing an outsized share, which is exactly the governance the platform team needs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A PriorityClass that assigns a low priority value to all training pods in the namespace.
Why it's wrong here
PriorityClass influences scheduling order and preemption, not the aggregate quantity of GPUs a namespace can hold. A low priority makes pods more likely to be evicted but does not enforce a hard ceiling, so it fails to guarantee that each namespace stays within a defined GPU budget.
- ✗
A PodSecurityPolicy that denies privileged containers in the namespace.
Why it's wrong here
PodSecurityPolicy, now removed in recent Kubernetes versions, controlled security contexts such as privileged mode and volume types. It has no concept of GPU quantity and cannot cap or reserve accelerators, so it does nothing to stop a namespace from exhausting the cluster's GPU pool.
- ✓
A LimitRange that sets a default and maximum nvidia.com/gpu value for containers in the namespace.
Why this is correct
LimitRange applies defaults and bounds to individual containers. Setting a maximum nvidia.com/gpu stops a single container from requesting an excessive number of GPUs, and the default ensures pods that omit a GPU request still receive a defined value, complementing the namespace-wide cap enforced by ResourceQuota.
- ✗
A NetworkPolicy that restricts traffic between pods in different namespaces.
Why it's wrong here
NetworkPolicy governs Layer 3 and Layer 4 connectivity between pods, not resource consumption. It cannot limit how many GPUs a namespace requests or prevent GPU monopolization, so it is irrelevant to enforcing per-namespace GPU capacity limits in this scenario.
- ✓
A ResourceQuota that specifies nvidia.com/gpu in its hard limits for each namespace.
Why this is correct
ResourceQuota supports extended resources, so declaring nvidia.com/gpu in the hard section caps the total GPU count a namespace may request. Once the quota is exhausted, further pods requesting GPUs are rejected at admission, which directly prevents one namespace from consuming more than its allotted share of cluster GPU capacity.
About these practice questions
This NCP-AIO question is part of Courseiva's 309-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.