NCP-AIO Workload Management Practice Question
A cloud operations team is using NVIDIA AI Enterprise with Kubernetes to deploy inference workloads. They want to ensure that GPU resources are allocated to pods only when explicitly requested, and that pods without GPU requests do not consume GPU resources. Which Kubernetes feature should they use to enforce this behavior?
⚠ Common exam trap
Candidates often confuse node-level scheduling constraints with resource-level allocation; node selectors only control placement, not whether a GPU is actually assigned.
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
✓
Define resource requests and limits for 'nvidia.com/gpu' in the pod specification.
Kubernetes requires pods to explicitly request GPU resources using the 'nvidia.com/gpu' resource name in their resource specifications. The scheduler then allocates GPUs only to those pods, ensuring that pods without such requests do not consume GPU resources even if they run on GPU nodes. This mechanism enforces the desired explicit allocation policy and is the standard way to manage GPU resources in Kubernetes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use node selectors to schedule pods only on GPU nodes.
Why it's wrong here
Node selectors are used to constrain pods to nodes with specific labels, such as GPU-enabled nodes. However, they do not control whether a pod requests GPU resources; a pod could be scheduled on a GPU node without requesting a GPU, potentially wasting resources. Node selectors alone do not enforce explicit GPU requests, so they do not meet the requirement.
- ✓
Define resource requests and limits for 'nvidia.com/gpu' in the pod specification.
Why this is correct
In Kubernetes, GPU resources are requested using the 'nvidia.com/gpu' resource name in the pod's resource requests and limits. When a pod specifies this, the scheduler allocates a GPU to it; pods without this request are not allocated GPUs, even if scheduled on GPU nodes. This enforces explicit GPU allocation and prevents pods from consuming GPU resources unintentionally, satisfying the team's requirement.
- ✗
Enable the GPU Operator's 'device plugin' to automatically inject GPU requests into all pods.
Why it's wrong here
The NVIDIA GPU Operator's device plugin advertises GPU resources to the Kubernetes scheduler, but it does not automatically inject GPU requests into pods. Pods must explicitly request GPUs; the device plugin only makes the resources available. Automatic injection would contradict the requirement that GPUs are allocated only when explicitly requested, and no such feature exists.
- ✗
Use a mutating admission webhook to add GPU requests to pods based on their namespace.
Why it's wrong here
A mutating admission webhook can modify pod specifications, but automatically adding GPU requests based on namespace would allocate GPUs to pods that did not explicitly ask for them. This violates the requirement that GPUs are allocated only when explicitly requested. While webhooks can enforce policies, this approach would not achieve the desired explicit allocation behavior.
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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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