NCP-AIO Multiple Select — 10 Questions

Question 1

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?

Accepted Answer

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

Increasing the CUDA_VISIBLE_DEVICES environment variable.

Answer

Setting a higher GPU limit in the Kubernetes manifest.

Answer

Deploying the NVIDIA Network Operator.

Question 2

An administrator is optimizing a cluster for AI model training using NVIDIA Base Command. Which TWO tasks are critical for ensuring consistent performance across the training nodes?

Accepted Answer

Standardizing NVIDIA driver and NCCL library versions across all nodes. Consistency in high-performance AI clusters depends on hardware synchronization and resource availability. Ensuring that all nodes run identical driver and firmware versions prevents subtle performance regressions during multi-node training. Furthermore, configuring GPUDirect RDMA is essential for reducing latency in inter-GPU communication, which is a major bottleneck in distributed training jobs. These steps are fundamental to maintaining high utilization and predictable training times across large-scale NVIDIA accelerated infrastructure.

Answer

Increasing the size of the swap partition on each node

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Disabling the NVIDIA persistence mode to save power

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Reducing the number of CUDA streams per training job

Question 3

Which THREE components are required for a container to successfully leverage NVIDIA GPUs on a Kubernetes cluster?

Accepted Answer

NVIDIA Container Toolkit. Successful GPU integration in Kubernetes relies on the interaction between the container engine, the NVIDIA driver, and the device plugin. The driver provides the kernel-level interface, the container runtime handles the low-level injection, and the device plugin advertises the hardware to the scheduler. These three parts must be correctly configured and compatible to ensure that containers can schedule and utilize GPUs effectively without manual configuration or persistent connectivity issues.

Answer

NVIDIA Data Center GPU Manager (DCGM)

Answer

Pre-installed CUDA Toolkit inside the container

Question 4

An AI researcher is debugging a multi-node training job using NCCL. Which TWO actions should they take to diagnose potential network-related performance degradation?

Accepted Answer

Set NCCL_DEBUG=INFO to monitor communication patterns and connection issues.. Debugging multi-node communication requires verifying both software configurations and physical network health. NCCL_DEBUG settings provide granular insight into connection establishment and topology detection, while NCCL tests provide baseline performance metrics. Identifying these issues early is essential for scaling models across large clusters, as network overhead can quickly become the dominant factor in training time during distributed synchronized gradient descent.

Answer

Increase the number of threads in the PyTorch DataLoader.

Answer

Switch from NCCL to MPI for all communication operations.

Answer

Lower the precision of the model to float16.

Question 5

Which TWO of the following are benefits of using containerized GPU workloads compared to bare-metal deployment?

Accepted Answer

Easier management of heterogeneous software dependencies.. Containerization provides portability, version control, and dependency isolation, which are essential for managing complex AI stacks. By packaging the environment, developers ensure that models run consistently across development, testing, and production clusters. This significantly reduces 'environment drift' and simplifies the management of different CUDA, cuDNN, and framework versions required by various research projects within the same shared infrastructure cluster.

Answer

Significant increase in raw GPU compute performance.

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Automatic elimination of GPU driver compatibility issues.

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Direct access to the GPU firmware for kernel customization.

Question 6

An administrator is deploying the NVIDIA GPU Operator on a Kubernetes cluster in an air-gapped environment. The cluster nodes have no internet access, and all container images must be pulled from a private registry. Which two actions are required to ensure a successful deployment? (Choose two.)

Accepted Answer

Configure the GPU Operator to use the private registry by setting the image repository in the Helm chart values.. Air-gapped deployments require that all container images are available in a private registry, and the GPU Operator must be configured to pull from that registry. Mirroring the images ensures availability, and setting the image repository in the Helm values directs the Operator to the correct location. These two actions are essential for a successful deployment without internet access.

Answer

Ensure that the private registry supports manifest lists for multi-architecture images.

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Disable the driver container and install drivers manually on each node.

Answer

Set the environment variable 'AIRGAP=true' in the operator's deployment.

Question 7

A research lab is deploying NVIDIA AI Enterprise on an air-gapped Kubernetes cluster. The cluster has no internet access, and all software must be installed from a local registry. The administrator plans to use the NVIDIA GPU Operator. Which two actions must be performed to ensure a successful deployment in this environment? (Choose two.)

Accepted Answer

Configure the GPU Operator to use the private registry by setting the appropriate Helm chart values.. In an air-gapped environment, the GPU Operator cannot reach public registries to pull container images. Therefore, all necessary images must be mirrored to a private registry, and the GPU Operator must be configured to use that registry via Helm values. These two actions ensure that the Operator can deploy its components without internet access. Other options are either not required or invalid for this scenario.

Answer

Install the NVIDIA GPU Operator using the --offline flag.

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Disable the GPU Operator's driver management and install the driver manually on each node.

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Ensure the cluster nodes have direct access to the NVIDIA licensing server for vGPU.

Question 8

A research team is submitting many short-lived experiment jobs to an NVIDIA-accelerated Kubernetes cluster. The operations team wants to reduce GPU idle time and improve overall utilization without modifying the training code. Which TWO approaches should the operations team implement? (Choose two.)

Accepted Answer

Use a gang-scheduling or batch scheduler with backfill so queued jobs start as soon as GPUs free up. Short-lived experiment jobs create idle GPU gaps. Time-slicing allows multiple such pods to share a device, and a batch scheduler with backfill keeps the queue moving so GPUs are assigned as soon as capacity frees. Both measures raise utilization without touching training code, which is exactly what the operations team needs.

Answer

Pin each experiment job to a dedicated physical GPU using node affinity

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Disable the NVIDIA device plugin and mount GPUs directly via hostPath

Answer

Increase the pod's nvidia.com/gpu request to reserve more GPU memory

Question 9

A cloud architect is deploying NVIDIA AI Enterprise on a vSphere cluster with multiple ESXi hosts, each containing NVIDIA A100 GPUs. The architect must enable GPU virtualization using NVIDIA vGPU. Which two components are required to support vGPU on the ESXi hosts? (Choose two.)

Accepted Answer

NVIDIA vGPU guest driver installed in each VM. To enable NVIDIA vGPU on VMware ESXi, the host must have the NVIDIA vGPU Manager VIB installed, which allows the hypervisor to partition the physical GPU. Each VM that uses a vGPU must also have the NVIDIA vGPU guest driver installed. These two components are essential; other tools like the GPU Operator or Container Toolkit are not used on ESXi.

Answer

NVIDIA GPU Operator for Kubernetes

Answer

NVIDIA CUDA Toolkit installed on the ESXi host

Answer

NVIDIA Container Toolkit installed on the ESXi host

Question 10

An AI researcher is deploying a multi-node training job using NCCL on an InfiniBand network. The job is suffering from intermittent latency spikes. Which TWO steps should the engineer perform to troubleshoot the network configuration?

Accepted Answer

Check IB link status and error counters using 'ibstat'.. Troubleshooting high-performance networking in distributed training requires checking for physical layer stability and protocol-level misconfigurations. Verifying InfiniBand counters helps identify packet drops or link flapping, while ensuring NCCL environment variables are correctly set for the specific topology avoids suboptimal routing. These steps ensure that the inter-GPU communication remains within the high-bandwidth low-latency envelope required for scaling deep learning workloads effectively.

Answer

Reset the GPU thermal throttling threshold.

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Update the OS kernel to the latest version.

Answer

Increase the system swap space to 512GB.