NCP-AIO Installation and Deployment Practice Test 6 — 15 Questions

Question 1

What is the primary role of the NVIDIA Data Center GPU Manager (DCGM) Exporter in a cloud-native monitoring stack?

Accepted Answer

To provide a Prometheus-compatible endpoint for GPU metrics.. The DCGM Exporter is the bridge between NVIDIA hardware metrics and monitoring systems like Prometheus. It gathers real-time telemetry from DCGM and exposes it in a format that Prometheus can scrape. This integration is vital for AI operations because it provides observability into GPU utilization, memory usage, and thermal health, enabling automated alerts and performance dashboards that are essential for maintaining a production-grade AI cluster.

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To act as a load balancer for GPU-accelerated traffic.

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To manage the deployment of containerized AI models.

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To encrypt data transmission between the GPU and the CPU.

Question 2

An administrator is deploying NVIDIA AI Enterprise on a bare-metal cluster. Which component must be installed first to ensure proper communication between the Kubernetes scheduler and the underlying GPU hardware?

Accepted Answer

NVIDIA GPU Operator. The NVIDIA GPU Operator is essential for automating the management of all NVIDIA software components in Kubernetes. By installing it first, the administrator ensures that the device plugin, monitoring tools, and drivers are correctly configured. This foundation is critical for scheduling GPU-accelerated pods, as the Kubernetes scheduler requires the device plugin to advertise available GPU resources to the cluster's API server, enabling seamless workload orchestration across the infrastructure.

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NVIDIA Triton Inference Server

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NVIDIA NeMo Framework

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NVIDIA Base Command Manager

Question 3

An administrator is preparing to deploy an NVIDIA AI Enterprise solution on an OpenShift cluster. Which TWO steps must be completed to ensure the NVIDIA drivers are loaded correctly on the worker nodes?

Accepted Answer

Deploy the NVIDIA GPU Operator via the OperatorHub.. On OpenShift, the NVIDIA GPU Operator leverages the Node Feature Discovery (NFD) and the Machine Config Operator to manage kernel modules. Ensuring these are configured correctly allows the driver container to load the proprietary NVIDIA kernel modules successfully. Proper node labeling is also required so the operator can target the correct hardware, ensuring that the driver is injected into the node's operating system environment during the boot process.

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Manually install NVIDIA drivers on every host OS.

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Install the NVIDIA Triton Inference Server.

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Set the Kubernetes scheduler to 'AlwaysPull'.

Question 4

When deploying the NVIDIA GPU Operator in a restricted-access environment (air-gapped), which THREE requirements must be addressed to ensure a successful installation?

Accepted Answer

Mirror all required images to a private registry.. In air-gapped environments, the inability to reach public registries is the primary failure point. Administrators must mirror all required container images to a local private registry, configure the operator to point to these local locations, and ensure that all necessary kernel headers are available locally for the driver build process. These steps ensure that the GPU Operator can complete its setup without external internet dependencies.

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Upgrade the host BIOS to the latest version.

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Install the NVIDIA Triton Inference Server first.

Question 5

An administrator is planning to monitor GPU utilization across a large cluster. Which component should be deployed to collect metrics that are compatible with Prometheus?

Accepted Answer

NVIDIA DCGM Exporter. NVIDIA DCGM (Data Center GPU Manager) is the industry-standard tool for collecting GPU health and telemetry data. By deploying the DCGM Exporter, the administrator enables the conversion of hardware telemetry into a Prometheus-friendly format. This integration is vital for observability, allowing teams to set up alerts and dashboards to track GPU usage, power consumption, and memory allocation across the entire fleet of accelerated computing nodes.

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NVIDIA GPU Operator

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NVIDIA Triton Inference Server

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NVIDIA CUDA Toolkit

Question 6

Which action is required when updating the NVIDIA driver on a node managed by the GPU Operator to ensure that running workloads are not interrupted abruptly?

Accepted Answer

Use the operator to perform a rolling update.. The GPU Operator supports seamless driver upgrades by cordoning and draining nodes. When an update is initiated, the operator gracefully moves workloads to other available nodes before updating the driver. This process prevents application crashes, ensures data integrity, and maintains cluster stability during maintenance windows, which is a key responsibility for AI operations professionals managing production-grade, long-running AI training or inference tasks on shared GPU resources.

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Manually stop all running pods.

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Reboot the entire cluster simultaneously.

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Delete the node object from the cluster.

Question 7

Which mechanism does the NVIDIA GPU Operator use to ensure that the driver installed on a worker node matches the specific architecture of the installed GPU hardware?

Accepted Answer

Node Feature Discovery (NFD).. The GPU Operator uses Node Feature Discovery (NFD) to identify the specific GPU hardware present on each node. By labeling nodes with these hardware characteristics, the operator can ensure that the correct driver and kernel modules are selected and deployed. This mapping is vital in heterogeneous clusters, where different nodes might require different driver builds, preventing installation errors and ensuring optimal performance across the entire fleet.

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NVIDIA System Management Interface (nvidia-smi).

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The container runtime's default configuration.

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Hardcoding the driver version in the deployment manifest.

Question 8

When troubleshooting an NVIDIA GPU Operator installation, which TWO locations should an administrator check to identify why the driver installation pod is failing?

Accepted Answer

The output of 'kubectl describe pod <driver-pod-name>'.. Checking the pod's logs and describe output is the standard diagnostic path. The 'describe' output shows events like ImagePullBackOff or scheduling failures, while the logs provide the specific error message from the driver installation script itself, such as kernel header mismatches or network timeouts. These two sources provide the necessary visibility to pinpoint whether the failure is infrastructure-related, configuration-based, or due to a missing environmental dependency.

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The contents of the /etc/kubernetes/manifests folder.

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The system-wide /var/log/syslog file on the control plane.

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The NVIDIA license server status page.

Question 9

Which component is responsible for exposing the GPU as a schedulable resource in a Kubernetes cluster?

Accepted Answer

NVIDIA Device Plugin. The Kubernetes device plugin is the interface that allows the kubelet to communicate with the GPU. It advertises the number of available GPUs on each node to the Kubernetes API server. When a pod requests a GPU, the scheduler uses this information to place the workload on the correct node. Without this plugin, Kubernetes is unaware of the GPU hardware, making it impossible to manage and allocate GPU resources for containerized workloads.

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NVIDIA Container Toolkit

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NVIDIA DCGM Exporter

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The Kubernetes Scheduler.

Question 10

An administrator observes that despite the GPU Operator being installed, the pods cannot access the GPU. What is the most likely cause if the NVIDIA container runtime is properly configured?

Accepted Answer

The host NVIDIA driver is not loaded.. If the runtime is configured, the most common remaining failure is the lack of the correct NVIDIA drivers on the host node. If the kernel modules are not loaded or the driver version is incompatible with the installed GPU, the runtime will be unable to successfully inject the device nodes. This is a common installation oversight where the operator might be deployed but the driver installation task failed or was skipped.

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The Kubernetes API server is down.

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The pod has too little memory requested.

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The container image is missing the CUDA library.

Question 11

Which security configuration is necessary when deploying NVIDIA GPUs in a multi-tenant environment to prevent unauthorized access between containers?

Accepted Answer

Enable MIG (Multi-Instance GPU).. In multi-tenant environments, using MIG (Multi-Instance GPU) is the standard method for hardware-level isolation. MIG allows a single physical GPU to be partitioned into multiple isolated instances, each with its own dedicated compute and memory. This ensures that one tenant's workload cannot interfere with or access the resources of another, providing a robust security boundary that software-only isolation cannot reliably guarantee in accelerated computing environments.

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Increase the Kubernetes pod memory limit.

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Use a privileged container.

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Install the latest version of CUDA.

Question 12

Which TWO of the following steps are essential when deploying the NVIDIA GPU Operator on a Kubernetes cluster to ensure that GPU resources are discoverable by the scheduler?

Accepted Answer

Enable the node feature discovery (NFD) service in the operator configuration.. The NVIDIA GPU Operator automates the lifecycle of NVIDIA software components, including the driver, toolkit, and device plugin. By configuring the operator to deploy the GPU device plugin and the Node Feature Discovery (NFD) service, the cluster gains the ability to identify GPU hardware and advertise it as an allocatable resource. Without these, the Kubernetes scheduler cannot place pods requiring GPU resources, leading to 'Pending' status for those workloads.

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Manually install the NVIDIA Container Toolkit on every worker node prior to operator deployment.

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Modify the Kubernetes API server manifest to include the NVIDIA-specific admission controller.

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Disable the default Kubernetes scheduler to allow the NVIDIA scheduler plugin to take over.

Question 13

Which component in the NVIDIA AI Enterprise stack is responsible for providing the necessary user-space libraries and binaries to run GPU-accelerated applications inside containers?

Accepted Answer

NVIDIA Container Toolkit. The NVIDIA Container Toolkit is essential because it allows the container runtime to interact with the host's NVIDIA drivers. It provides the necessary libraries and the container runtime wrapper to expose GPUs inside the container environment. Without this toolkit, applications inside a container cannot access the GPU hardware, even if the host has the correct drivers installed, making it a critical deployment component.

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NVIDIA GPU Operator

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NVIDIA License System

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NVIDIA Unified Fabric Manager

Question 14

During the deployment of an AI model training workload on a multi-node cluster, the administrator notices that inter-node communication is significantly slower than expected. Which deployment aspect should be investigated first?

Accepted Answer

Check the NCCL_DEBUG and network interface configuration for the training job.. NCCL (NVIDIA Collective Communications Library) is the primary engine for inter-node communication in distributed training. Misconfiguration of the network interface or the underlying fabric provider (e.g., InfiniBand or RoCE) will lead to significant performance bottlenecks. Investigating the NCCL configuration and the network topology ensures that the GPUs are utilizing the highest bandwidth available, which is vital for preventing training jobs from stalling during gradient synchronization across multiple nodes.

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Verify that the GPU memory usage is below 50% on all nodes.

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Restart the Kubernetes API server to refresh the node connection states.

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Update the NVIDIA driver to the latest gaming-optimized release.

Question 15

Refer to the exhibit. An administrator attempts to deploy a GPU-based pod, but it remains in the 'Pending' state. What is the most likely cause based on the error log?

Accepted Answer

The NVIDIA device plugin is not correctly advertising GPU resources.. The error 'Insufficient nvidia.com/gpu' indicates that the Kubernetes scheduler cannot find a node with available GPU resources that match the pod's request. This typically happens when the device plugin is not correctly reporting resource availability to the scheduler, or the cluster is over-provisioned. Resolving this requires ensuring the GPU Operator is running and that the device plugin has successfully registered the GPUs with the Kubelet on the worker nodes.

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The pod is requesting more CPU cores than the node can provide.

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The node is in a 'NotReady' state due to high disk latency.

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The pod security policy prohibits the use of GPU-accelerated containers.