NCP-AIO Workload Management Practice Question
When designing a workload management strategy for multi-tenant AI training, what is the most effective way to ensure isolation between different tenants using the same physical GPU nodes?
⚠ Common exam trap
Candidates often suggest software-level resource limits (like cgroups) for GPU isolation, which do not provide the same level of hardware-enforced memory and compute partitioning as NVIDIA MIG.
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 NVIDIA MIG to partition hardware at the compute and memory level.
Using NVIDIA Multi-Instance GPU (MIG) is the most robust method for hardware-level isolation. MIG partitions a single physical GPU into multiple independent instances, each with its own memory and compute resources. This allows multiple tenants to run workloads simultaneously on the same hardware without interfering with each other's performance, ensuring predictable and secure resource allocation for multi-tenant AI environments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set strict Kubernetes memory limits on every pod to prevent memory leakage.
Why it's wrong here
Kubernetes memory limits prevent a process from exceeding a threshold, but they do not provide true GPU hardware isolation. A rogue process can still impact GPU performance or monopolize the GPU compute cores. MIG provides the isolation that memory limits cannot achieve in a shared GPU environment.
- ✓
Implement NVIDIA MIG to partition hardware at the compute and memory level.
Why this is correct
MIG provides spatial and temporal hardware partitioning, ensuring that individual workloads have their own dedicated hardware paths. This prevents performance degradation caused by noisy neighbors and provides security by isolating memory and compute resources, which is critical for multi-tenant clusters that require predictable performance and isolation.
- ✗
Use Kubernetes namespaces to logically separate the tenants.
Why it's wrong here
Namespaces provide logical separation, not physical isolation. They help with resource accounting and RBAC but do not prevent a pod in one namespace from saturating the GPU resource on the host node. Physical isolation must be enforced by hardware-level mechanisms like MIG or strictly partitioned node pools.
- ✗
Enable GPU sharing through the NVIDIA Container Toolkit's time-slicing configuration.
Why it's wrong here
Time-slicing allows multiple pods to use a GPU by switching between them, but it does not provide resource isolation. One pod can still monopolize the GPU cycles during its time slice, causing performance inconsistency for other tenants. It is less secure and less performant than hardware-level MIG partitioning.
About these practice questions
Courseiva writes every NCP-AIO question from scratch — 309 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or 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.