NCP-AIO Installation and Deployment Practice Test 7 — 15 Questions

Question 1

When deploying NVIDIA AI Enterprise, why is the use of the NVIDIA NGC Catalog recommended over public container repositories?

Accepted Answer

NGC images are curated and optimized for NVIDIA hardware compatibility.. The NGC Catalog provides verified, performance-optimized, and security-scanned container images specifically tailored for the NVIDIA AI Enterprise stack. These images contain all necessary dependencies and are guaranteed to be compatible with supported GPU drivers. Using these images reduces the risk of deployment failures caused by library version mismatches, missing dependencies, or unoptimized software versions that are common in generic, public repositories, thereby ensuring a reliable production-grade AI environment.

Answer

NGC images are the only way to bypass the need for a valid NVIDIA license.

Answer

NGC images are pre-configured to automatically perform hardware diagnostics.

Answer

NGC images can be deployed without any need for the NVIDIA Container Toolkit.

Question 2

An administrator is tasked with deploying a multi-node training job using the NVIDIA GPU Operator. Which configuration must be present to ensure that pods are scheduled on nodes with identical GPU architectures to prevent performance degradation?

Accepted Answer

Use node affinity labels based on NFD-provided hardware information.. To maintain high-performance, synchronized training, all participating nodes should share the same GPU architecture and interconnect type (e.g., NVLink or InfiniBand). Using Kubernetes node affinity or anti-affinity rules combined with the hardware labels automatically generated by the Node Feature Discovery (NFD) service ensures that the scheduler places the job exclusively on compatible nodes. This prevents the training from falling back to slower, sub-optimal communication paths or heterogeneous hardware modes.

Answer

Enable the 'auto-scaling' feature in the NVIDIA GPU Operator.

Answer

Increase the timeout values for the NCCL collective communication operations.

Answer

Set the 'nvidia.com/gpu' resource limit to zero on all nodes except the master node.

Question 3

Which command is used to verify that the NVIDIA GPU Operator has successfully installed the necessary components on a Kubernetes node?

Accepted Answer

kubectl get pods -n gpu-operator. The 'kubectl get pods -n gpu-operator' command is the primary method to check the status of the operator and its managed components, such as the device plugin and driver daemonsets. This step is essential because it confirms that the operator's control loop has successfully completed the deployment, ensuring that the GPU software stack is active and ready to handle incoming AI workload scheduling requests.

Answer

nvidia-smi check-components

Answer

docker inspect nvidia-gpu-operator

Answer

kube-config verify --gpu

Question 4

An AI platform engineer is preparing a fleet of NVIDIA DGX H100 systems for production workloads using the NVIDIA Base Command Manager (BCM). During initial bare-metal provisioning via PXE boot, the provisioning server successfully hands out IP addresses, but nodes consistently fail during the OS image deployment phase, throwing a kernel panic related to missing storage drivers. Which deployment step must be verified or corrected to ensure successful hardware-specific image deployment?

Accepted Answer

Verify that the target operating system image profile assigned in Base Command Manager includes the necessary storage controller modules and hardware support packages for the specific server model.. NVIDIA Base Command Manager relies heavily on matching the hardware profile of complex servers like DGX H100 with the correct custom software image and specialized storage drivers. If the deployment image lacks the required kernel modules for modern high-performance NVMe controllers, PXE booting fails at the kernel load stage. Ensuring the software image profile incorporates the exact hardware-specific drivers resolves this mismatch, allowing enterprise-grade bare-metal automation to complete seamlessly across the GPU cluster.

Answer

Reconfigure the DHCP server scope options to extend lease times and include custom vendor-class identifiers for the provisioning daemon.

Answer

Update the system BIOS Unified Extensible Firmware Interface boot order to prioritize internal redundant array of independent disks storage over network interfaces.

Answer

Modify the dynamic port forwarding settings on the top-of-rack management switches to allow uninterrupted trivial file transfer protocol block size extensions.

Question 5

A platform team is deploying the NVIDIA GPU Operator on a Kubernetes cluster to manage GPU nodes. They want the Operator to automatically install the NVIDIA driver, the container toolkit, and the device plugin on each GPU node. Which component of the GPU Operator is responsible for installing the NVIDIA driver on the host?

Accepted Answer

NVIDIA Driver Container. The GPU Operator automates the deployment of all NVIDIA software components on Kubernetes. The Driver Container is specifically designed to install the NVIDIA driver on the host. It runs as a DaemonSet and installs the driver into the host's filesystem, eliminating the need for manual driver installation. The other components handle container runtime integration, resource advertisement, and validation.

Answer

NVIDIA Container Toolkit

Answer

NVIDIA Device Plugin

Answer

NVIDIA GPU Operator Validator

Question 6

A platform engineer is preparing a Kubernetes cluster to run AI training jobs that require GPU access. The cluster nodes have NVIDIA GPUs, and the engineer wants the GPU Operator to manage the driver lifecycle. Which component must be installed on the host nodes to allow the GPU Operator to load kernel modules and create device nodes?

Accepted Answer

NVIDIA GPU Operator Driver Container. The GPU Operator uses a driver container to manage the NVIDIA driver on host nodes. This container loads kernel modules, creates device nodes, and ensures the driver version matches the operator's requirements. The NVIDIA Container Toolkit and Device Plugin are also deployed by the operator but serve different purposes: the toolkit enables container GPU access, and the device plugin advertises GPUs to Kubernetes. The driver container is specifically responsible for the driver lifecycle, making it the correct component.

Answer

NVIDIA Kubernetes Device Plugin

Answer

NVIDIA Container Toolkit

Answer

NVIDIA GPU Driver

Question 7

An administrator is preparing a Kubernetes cluster for AI workloads and needs to ensure that the NVIDIA GPU Operator can be installed. The cluster nodes have NVIDIA GPUs, and the administrator wants to verify that the nodes are ready. Which command should the administrator run to check if the NVIDIA driver is already loaded on a node?

Accepted Answer

nvidia-smi. To verify that the NVIDIA driver is loaded on a node, the administrator should run nvidia-smi. This command queries the driver and displays GPU details such as driver version, GPU utilization, and memory usage. It is the definitive check for driver functionality. Other commands may show GPU hardware or Kubernetes resources but do not confirm driver operation.

Answer

lspci | grep -i nvidia

Answer

kubectl get nodes -o wide

Answer

kubectl describe node <node-name>

Question 8

A financial services company is deploying NVIDIA AI Enterprise on a Kubernetes cluster with strict security policies. They need to ensure that GPU workloads are isolated and that the NVIDIA GPU Operator components are deployed with least privilege. Which feature of the NVIDIA GPU Operator allows administrators to define granular permissions for its components?

Accepted Answer

Role-Based Access Control (RBAC). RBAC is the Kubernetes mechanism for defining granular permissions. The NVIDIA GPU Operator deploys components with specific ServiceAccounts and RBAC roles that grant only the permissions needed to perform their functions. This aligns with least-privilege principles and is critical for security-sensitive deployments. Other options are either deprecated, network-focused, or platform-specific.

Answer

Security Context Constraints

Answer

PodSecurityPolicy

Answer

Network Policies

Question 9

An AI operations engineer is preparing a Kubernetes cluster to run GPU-accelerated inference workloads using the NVIDIA GPU Operator. The cluster nodes already have NVIDIA data center GPUs installed, and the engineer wants to avoid installing the driver manually on each node. Which component of the GPU Operator is responsible for automatically deploying the NVIDIA driver on worker nodes?

Accepted Answer

NVIDIA GPU Operator's driver container. The driver container within the GPU Operator automates the deployment and management of NVIDIA drivers on Kubernetes nodes. It runs as a DaemonSet and ensures the correct driver version is loaded without manual intervention. This simplifies operations and maintains consistency across the cluster, which is essential for AI workloads that depend on specific driver capabilities.

Answer

NVIDIA Device Plugin

Answer

NVIDIA DCGM Exporter

Answer

NVIDIA Container Toolkit

Question 10

A platform engineer is preparing a bare-metal Kubernetes cluster to run GPU-accelerated AI workloads using the NVIDIA GPU Operator. The cluster nodes have NVIDIA Ampere GPUs and run Ubuntu 22.04 with containerd as the container runtime. The engineer wants to avoid installing any NVIDIA drivers or CUDA components directly on the host. Which GPU Operator configuration should be used to achieve this?

Accepted Answer

Deploy the GPU Operator with the default configuration, which includes the driver container.. The NVIDIA GPU Operator is designed to manage the full stack of GPU software, including the driver, by running a driver container on each node. In a default deployment, the driver container builds and loads the kernel modules, so no manual host driver installation is needed. This aligns with the requirement to keep the host clean and let the Operator handle everything.

Answer

Set driver.enabled=false and rely on pre-installed host drivers.

Answer

Use the operator's 'driver' Helm chart with 'driver.enabled=true' but set 'driver.usePrecompiled=true'.

Answer

Install the NVIDIA Container Toolkit manually and disable the operator's driver management.

Question 11

A company is deploying NVIDIA AI Enterprise on a vSphere cluster with multiple ESXi hosts, each containing NVIDIA A100 GPUs. They need to enable vGPU functionality for virtual machines running AI workloads. Which configuration step is required on the ESXi host to allow vGPU assignment to VMs?

Accepted Answer

Install the NVIDIA vGPU Manager on the ESXi host and set the graphics type to shared or direct.. Enabling vGPU on ESXi requires the NVIDIA vGPU Manager VIB and setting the GPU graphics type appropriately. This allows the hypervisor to present virtual GPUs to VMs. Alternatives like the Container Toolkit or GPU Operator are not applicable to ESXi host-level configuration, and SR-IOV is not the mechanism used for vGPU in this context.

Answer

Install the NVIDIA GPU Operator on the ESXi host and create a custom resource for vGPU.

Answer

Enable SR-IOV on the ESXi host and configure virtual functions for each VM.

Answer

Install the NVIDIA Container Toolkit on the ESXi host and configure the Docker daemon.

Question 12

A platform engineer is preparing a bare-metal Ubuntu 22.04 server with four A100 GPUs for an NVIDIA AI Enterprise deployment. The GPUs are not yet visible to the operating system tooling. Which command should the engineer run to confirm the driver loaded successfully and that all four GPUs are enumerated with their current driver version?

Accepted Answer

nvidia-smi. The NVIDIA kernel driver is the foundation of every AI Enterprise workload, and nvidia-smi is the standard utility that both triggers a driver query and renders the full GPU inventory with driver and CUDA versions. Administrative toolkits such as DCGM or the container toolkit sit above the driver and only report meaningful data once the driver has bound successfully to all adapters.

Answer

dcgmi discovery -l

Answer

nvidia-container-cli --info

Answer

lspci -d 10de:

Question 13

A platform engineer is preparing an Ubuntu 22.04 server that will host GPU-accelerated inference containers managed by containerd (not Docker). The team wants the NVIDIA Container Toolkit to expose GPUs to those containers. After installing the toolkit packages, which action must the engineer take so that containerd actually invokes the NVIDIA runtime for GPU workloads?

Accepted Answer

Run nvidia-ctk runtime configure --runtime=containerd and restart the containerd service.. Registering the NVIDIA runtime with the container engine is the essential post-install step. The nvidia-ctk runtime configure command writes the runtime entry and default-runtime setting into containerd's config.toml, and containerd must be restarted to apply it. Merely installing packages or setting container environment variables does not change which runtime containerd uses, so GPU devices remain invisible to containers until the engine configuration is updated and reloaded.

Answer

Install the nvidia-container-runtime package and symlink it as /usr/bin/runc on the host.

Answer

Add the user to the video group and grant read-write access to /dev/nvidiactl.

Answer

Set the environment variable NVIDIA_VISIBLE_DEVICES=all in the host shell profile and reboot the node.

Question 14

An AI operations team is deploying the NVIDIA GPU Operator on a Kubernetes cluster that uses containerd as the container runtime. The cluster nodes have NVIDIA GPUs, and the team wants to ensure that GPU workloads can request GPU resources. After installing the operator, they notice that pods requesting 'nvidia.com/gpu' remain in Pending state. Which component of the GPU Operator is most likely misconfigured or missing?

Accepted Answer

The NVIDIA device plugin, which advertises GPU resources to the Kubernetes API server.. The NVIDIA device plugin is a DaemonSet that runs on each node and registers GPUs as extended resources. When it is missing or misconfigured, the Kubernetes API server has no knowledge of available GPUs, so any pod requesting 'nvidia.com/gpu' cannot be scheduled. Ensuring the device plugin is healthy and running is essential for GPU scheduling.

Answer

The Kubernetes scheduler configuration, which may not be aware of GPU resources.

Answer

The NVIDIA Container Toolkit, which enables containers to access GPUs.

Answer

The GPU Operator's driver container, which loads the NVIDIA kernel modules.

Question 15

A system administrator is installing the NVIDIA Container Toolkit on a standalone Ubuntu server to run GPU-accelerated containers. After installation, they want to verify that the toolkit is correctly configured. Which command should they run to test GPU access from a container?

Accepted Answer

docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi. To verify that the NVIDIA Container Toolkit is correctly configured, the administrator should run a container with GPU access and execute nvidia-smi inside it. The command docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi uses the --gpus all flag to expose all GPUs, and the nvidia-smi output confirms that the container can see and use the GPUs. This tests the entire stack from Docker to the toolkit. Other commands only check installation or host-level GPU status.

Answer

nvidia-smi

Answer

systemctl status nvidia-container-runtime

Answer

nvidia-container-cli --version