NCP-AIO Administration Practice Question
An administrator is responsible for maintaining an NVIDIA AI Enterprise cluster and needs to ensure high availability of GPU resources for critical inference workloads. Which two practices should the administrator implement? (Choose two.)
⚠ Common exam trap
The trap here is equating resource sharing or isolation features like MIG or time-slicing with high availability, when they do not protect against hardware failure.
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
✓
Implement pod disruption budgets to maintain a minimum number of available replicas
High availability for inference workloads requires spreading pods across multiple nodes to avoid single points of failure and using pod disruption budgets to maintain minimum replica counts during disruptions. Node anti-affinity ensures distribution, while PDBs protect against voluntary evictions. Time-slicing, MIG, and single-node concentration do not provide fault tolerance against node or GPU failures.
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 GPU time-slicing to allow multiple pods to share a single GPU
Why it's wrong here
GPU time-slicing allows multiple workloads to share a GPU by time-division multiplexing, which can improve utilization but does not provide high availability. If the GPU fails, all pods sharing it are affected. Time-slicing is useful for increasing density, but it does not mitigate hardware failures or node outages, so it is not a high-availability practice.
- ✗
Schedule all inference pods on a single node with multiple GPUs to reduce network latency
Why it's wrong here
Concentrating all pods on a single node creates a single point of failure. If that node goes down, all inference workloads become unavailable. While reducing network latency might be a goal, it compromises high availability. Spreading workloads across nodes is a better practice for critical services.
- ✗
Use NVIDIA MIG to partition a GPU into isolated instances for each workload
Why it's wrong here
MIG (Multi-Instance GPU) partitions a single GPU into isolated instances, providing QoS and isolation. However, it does not provide high availability because all instances reside on the same physical GPU. If that GPU fails, all MIG instances are lost. MIG is beneficial for isolation and resource guarantees, but it does not protect against hardware failure.
- ✓
Implement pod disruption budgets to maintain a minimum number of available replicas
Why this is correct
Pod disruption budgets (PDBs) ensure that a specified minimum number of pods remain available during voluntary disruptions, such as node drains or upgrades. This helps maintain service availability for critical inference workloads. By defining a PDB, the administrator can prevent simultaneous eviction of too many replicas, thus supporting high availability.
- ✓
Configure node affinity and anti-affinity rules to spread pods across multiple nodes
Why this is correct
Node affinity and anti-affinity rules allow administrators to control pod placement. By using anti-affinity, pods can be spread across different nodes, reducing the risk of a single node failure affecting all replicas. This improves availability and ensures that GPU resources are utilized across the cluster. It is a standard Kubernetes mechanism for high availability.
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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.