NCP-AIO Workload Management Practice Question
When managing GPU resources in a shared cluster, which configuration best prevents 'noisy neighbor' scenarios where one GPU task consumes all available memory bandwidth?
⚠ Common exam trap
Candidates often choose software-level container limits or basic Kubernetes namespaces alone, forgetting that true bandwidth isolation requires hardware-level partitioning like NVIDIA MIG to prevent high-throughput tasks from monopolizing shared memory paths.
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
✓
Implementing NVIDIA MIG and strictly defining resource limits.
Using a combination of NVIDIA MIG and Kubernetes resource quotas provides the strongest isolation. By enforcing hardware-level partitioning via MIG, you ensure that memory bandwidth is strictly bounded for each slice. This is vital for AI Ops because it prevents high-throughput training jobs from starving small inference requests, ensuring stable latency across the entire cluster environment and improving total system reliability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increasing the CUDA_VISIBLE_DEVICES environment variable.
Why it's wrong here
CUDA_VISIBLE_DEVICES only restricts which physical GPUs a process can 'see' and access. It does not impose limits on memory bandwidth or throughput consumption on the assigned GPU, meaning a process can still saturate the GPU's memory interface and degrade performance for other tasks sharing that specific hardware.
- ✓
Implementing NVIDIA MIG and strictly defining resource limits.
Why this is correct
MIG partitions the GPU at the hardware level, providing dedicated memory and compute paths. When combined with Kubernetes resource limits, it ensures that workloads are physically constrained to their slice, preventing a single process from monopolizing memory bandwidth or compute cycles, thus effectively eliminating the noisy neighbor problem.
- ✗
Setting a higher priority class for inference pods.
Why it's wrong here
Priority classes govern pod scheduling and preemption, not the runtime resource consumption of a process. A high-priority pod can still be a noisy neighbor if it is poorly written or overly aggressive, as priority does not regulate the amount of memory bandwidth or cache it uses while running.
- ✗
Configuring the NVIDIA DCGM Exporter alerts.
Why it's wrong here
DCGM Exporter alerts are purely diagnostic and reactive; they notify administrators of existing issues. They do not proactively prevent noisy neighbor scenarios, as they lack the enforcement mechanisms necessary to throttle or isolate resource consumption during the execution of a process on the GPU hardware.
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.