NCP-AIO Workload Management Practice Question
Which TWO methods are effective for enforcing GPU resource isolation in a multi-tenant NVIDIA Kubernetes environment?
⚠ Common exam trap
Candidates tend to pick only software scheduling or only hardware partitioning, forgetting that robust multi-tenant isolation requires a combination of both MIG and Kubernetes controls.
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
✓
Enabling NVIDIA Multi-Instance GPU (MIG) for hardware partitioning.
Resource isolation is paramount in multi-tenant environments to prevent noisy neighbor effects where one workload consumes disproportionate GPU cycles. NVIDIA Multi-Instance GPU (MIG) provides hardware-level isolation for partitioning, while Kubernetes-native device plugins with affinity and tolerations provide software-level scheduling control. These mechanisms combined ensure predictable performance and security, preventing cross-tenant interference during intensive training or inference cycles in shared GPU infrastructure clusters.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enabling NVIDIA Multi-Instance GPU (MIG) for hardware partitioning.
Why this is correct
MIG allows a single GPU to be carved into multiple independent instances, each with its own dedicated memory, cache, and compute cores. This provides strict hardware-enforced isolation, ensuring that one workload cannot interfere with the performance or data security of another tenant running on the same physical chip.
- ✗
Configuring standard Docker cgroups for GPU memory limits.
Why it's wrong here
Docker cgroups are designed primarily for system CPU and RAM isolation. They do not have native awareness of GPU hardware resources, meaning they cannot limit or isolate memory or compute cycles on NVIDIA devices. Implementing cgroups alone will fail to prevent resource contention on the underlying GPU architecture.
- ✓
Applying Kubernetes Taints, Tolerations, and Node Affinity.
Why this is correct
Taints, tolerations, and node affinity allow administrators to restrict workload placement. By isolating specific workloads to dedicated GPU nodes or node pools, you prevent unauthorized or resource-heavy jobs from landing on infrastructure reserved for critical services, effectively managing resource consumption through intelligent scheduling and placement policies.
- ✗
Implementing standard OS-level priority queuing via 'nice'.
Why it's wrong here
The 'nice' utility manages CPU scheduling priorities at the OS kernel level. It has no visibility into GPU work queues or compute task scheduling. Consequently, it cannot influence or prioritize GPU-bound processes, making it ineffective for managing resource contention in high-performance AI training or inference workload scenarios.
- ✗
Setting a global environment variable for GPU frequency scaling.
Why it's wrong here
Global GPU frequency scaling affects the entire device clock rate and is not an isolation mechanism. Changing this would impact all workloads running on that GPU equally, rather than providing the granular control required for multi-tenant environments where distinct isolation levels are necessary for different user tasks.
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.