NCP-AIO Workload Management Practice Question
An administrator is optimizing multi-GPU utilization. Which TWO of the following configurations allow multiple containers to share a single physical GPU on a supported NVIDIA architecture?
⚠ Common exam trap
Candidates frequently confuse software-level multiplexing options like Time-Slicing with hypervisor features or mistake them for hardware-partitioning mechanisms like MIG, failing to recognize that both are valid sharing methods.
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 Time-Slicing in the GPU Operator configuration.
Multi-Instance GPU (MIG) and Time-Slicing are the primary methods for sharing physical GPU resources. MIG provides hardware-level isolation, while Time-Slicing provides software-based multiplexing. Understanding these options is essential for AI operations, as it allows administrators to maximize hardware ROI by supporting smaller workloads that do not require an entire A100 or H100 GPU, effectively increasing the density of the training or inference environment.
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 Time-Slicing in the GPU Operator configuration.
Why this is correct
Time-Slicing allows multiple pods to share a GPU by switching context at rapid intervals. This is a software-based approach that enables oversubscription, allowing smaller workloads to execute concurrently on the same hardware, which is highly beneficial for development environments or low-throughput inference tasks that don't need full GPU power.
- ✓
Configuring Multi-Instance GPU (MIG) profiles.
Why this is correct
MIG allows a single physical GPU to be partitioned into multiple isolated instances. Each instance appears as a separate GPU to the OS, providing strict quality-of-service and fault isolation. This is the preferred method for workloads requiring predictable performance and security when sharing hardware between different users or teams.
- ✗
Increasing the CUDA_VISIBLE_DEVICES environment variable.
Why it's wrong here
CUDA_VISIBLE_DEVICES restricts which GPUs a process can access; it does not enable resource sharing. Setting this variable only filters the visible devices. It cannot partition a single GPU or allow multiple containers to access the same physical hardware concurrently for parallel processing in an isolated manner.
- ✗
Setting a higher GPU limit in the Kubernetes manifest.
Why it's wrong here
Increasing the GPU limit only allocates more resources to a single container. It does not facilitate sharing or partitioning of a physical GPU. In fact, requesting a higher limit typically consumes the entire GPU, preventing other containers from utilizing the device, which is the opposite of the requested goal.
- ✗
Deploying the NVIDIA Network Operator.
Why it's wrong here
The Network Operator focuses on high-speed interconnects like InfiniBand and RoCE, managing network resources and performance. It does not provide functionality for GPU resource partitioning or sharing. Networking and compute resource management are distinct domains in cluster architecture, and this component does not facilitate the sharing of 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.