NCP-AIO Workload Management Practice Question
Which TWO strategies should an administrator implement to ensure fair resource scheduling in a multi-tenant NVIDIA cluster using Kubernetes and the NVIDIA device plugin?
⚠ Common exam trap
Candidates often select only software scheduling flags or generic pod limits, missing the necessary combination of namespace-level capacity caps and explicit job priority classes required for multi-tenant fairness.
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 Kubernetes ResourceQuotas to limit the total number of GPUs per namespace.
Fair scheduling in multi-tenant environments requires both hard limits to prevent resource monopolization and priority-based mechanisms to ensure critical jobs progress. By combining ResourceQuotas for capacity management and PriorityClasses for job scheduling, admins can prevent a single user from starving the cluster while maintaining high performance for latency-sensitive inference or urgent training tasks during peak usage windows.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configure Kubernetes ResourceQuotas to limit the total number of GPUs per namespace.
Why this is correct
ResourceQuotas provide a hard ceiling on the aggregate compute resources a namespace can consume. This prevents a single tenant from launching an excessive number of pods that might exhaust cluster GPU capacity, ensuring that other tenants maintain access to sufficient hardware for their own operational and development needs.
- ✗
Implement static partitioning for all GPUs in the cluster.
Why it's wrong here
Static partitioning locks resources to specific workloads, which is inefficient in dynamic AI environments. This approach prevents resource sharing and leads to fragmentation, where idle GPUs cannot be reassigned to other jobs. Dynamic scheduling is preferred for better resource utilization, whereas static partitioning creates rigid, under-utilized resource silos.
- ✓
Define Kubernetes PriorityClasses to influence job preemption policies.
Why this is correct
PriorityClasses allow administrators to assign importance to workloads, enabling the scheduler to preempt lower-priority pods when cluster resources are constrained. This ensures that high-importance training runs or production inference services are prioritized, maintaining business continuity even when the cluster is under heavy load from lower-priority experimental jobs.
- ✗
Use the NVIDIA Triton Inference Server to manage all batch processing.
Why it's wrong here
Triton is a specialized inference execution engine, not a cluster-level resource scheduler. While it optimizes model execution, it cannot manage scheduling across multiple nodes or enforce cross-tenant resource quotas. Its primary function is model serving, which is distinct from the infrastructure management required for fair resource scheduling.
- ✗
Disable the NVIDIA device plugin to allow direct node access.
Why it's wrong here
Disabling the device plugin removes the ability for Kubernetes to manage and schedule GPU resources, leading to potential conflicts and unstable deployments. Direct access bypasses the necessary abstraction layers that allow the scheduler to monitor and enforce hardware health, making it impossible to perform meaningful workload management.
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.