NCP-AIO Practice Test 5 — 10 Questions

Question 1

When deploying NVIDIA containers using the NVIDIA Container Toolkit, what is the primary function of the 'nvidia-container-runtime'?

Accepted Answer

It exposes the host's NVIDIA GPUs and driver libraries to the container environment.. The nvidia-container-runtime is a crucial component that allows Docker containers to interface with host GPUs. By modifying the container runtime specification, it ensures that the necessary device nodes and NVIDIA driver libraries are injected into the container namespace at startup. This enables seamless hardware acceleration for AI applications without requiring users to manually manage drivers or complex device path configurations inside their container images.

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It automatically recompiles the application code for the specific GPU architecture found on the host.

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It manages the lifecycle of the GPU driver installation on the host operating system.

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It monitors the temperature and power consumption of the GPUs during container execution.

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 operations engineer notices that an inference container on an A100 is intermittently returning stale predictions after a model update. The container mounts the model directory from a host path, and the update process replaces files in place. Which change most reliably prevents the stale predictions?

Accepted Answer

Use a versioned, immutable model artifact and restart or roll the inference container so it loads the new version atomically.. When model files are overwritten in place, a running inference process keeps using the weights it already loaded, so clients see stale results until the process restarts. Treating each model as an immutable, versioned artifact and performing a container restart or rolling deployment guarantees an atomic switch to a consistent set of weights. This removes the timing race between the update and the load, which is the actual source of the stale predictions.

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Increase the container's shared memory size so the model can be cached entirely in RAM.

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Reduce the inference batch size so each prediction reads the model from disk again.

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Enable read-only mounts for the model directory to block writes from the container.

Question 4

Which component is strictly necessary for managing NVIDIA AI Enterprise licensing across a distributed cluster of nodes?

Accepted Answer

NVIDIA License System (NLS) Instance.. The NVIDIA License System (NLS) is the centralized authority for managing product entitlements. In a distributed environment, nodes must reach this server to validate their software keys. This component is crucial because it decouples the license management from the individual worker nodes, allowing for flexible scaling and centralized compliance reporting, which are essential in enterprise AI infrastructure where hardware resources are frequently provisioned and decommissioned.

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NVIDIA CUDA Toolkit.

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NVIDIA Container Toolkit.

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NVIDIA Deep Learning GPU Training System (DIGITS).

Question 5

An administrator manages a Kubernetes cluster where a training job repeatedly fails with an OutOfMemory error on the GPU even though the pod requests one nvidia.com/gpu. DCGM metrics show that another pod on the same node is consuming GPU memory concurrently. GPU sharing via time-slicing is enabled cluster-wide. Which action should the administrator take to prevent this cross-pod interference while preserving the ability to share GPUs among trusted inference workloads?

Accepted Answer

Disable time-slicing on the node pool hosting training jobs and use MIG or dedicated GPUs for those workloads. Time-slicing multiplexes a GPU across pods without memory partitioning, so one workload can starve another and trigger OutOfMemory errors. The reliable fix is to separate untrusted or memory-intensive training workloads onto dedicated GPUs or MIG instances, while leaving time-slicing for inference workloads where memory contention is acceptable and controlled.

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Apply a ResourceQuota limiting nvidia.com/gpu to one per namespace

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Set the CUDA_VISIBLE_DEVICES environment variable manually in the training pod spec

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Increase the pod's nvidia.com/gpu request to two GPUs

Question 6

A platform team runs an NVIDIA GPU Operator-managed Kubernetes cluster shared by two research groups. Group A's pods request `nvidia.com/gpu: 1` and are scheduled correctly, but Group B's pods that omit any GPU resource request are also landing on GPU nodes and consuming host memory and CPU, degrading Group A's jobs. The team wants Group B's non-GPU pods to stop consuming capacity on the GPU node pool without changing Group B's manifests. Which action should the administrator take?

Accepted Answer

Apply a taint such as `nvidia.com/gpu=present:NoSchedule` to the GPU nodes and add a matching toleration to Group A's pod templates.. Dedicating a node pool to GPU consumers is done at the scheduler layer: a taint on the GPU nodes repels every pod that does not carry a matching toleration. Because Group B's manifests cannot be changed, repelling rather than attracting is the only workable direction, and adding tolerations to Group A's templates is a one-time change under the platform team's control. Quotas, driver flags, and plugin options all operate after or outside scheduling and cannot keep non-GPU pods off those nodes.

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Set `NVIDIA_DRIVER_CAPABILITIES=compute,utility` on the GPU Operator DaemonSet so non-GPU pods cannot attach to the device.

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Create a `ResourceQuota` in each namespace that caps `requests.nvidia.com/gpu` at zero for Group B.

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Enable the `nvidia` device plugin's `--fail-on-init-error=false` flag so unresolvable GPU requests fall back to CPU scheduling.

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.

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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.

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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

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

Question 9

An AI operations team is using NVIDIA DCGM to monitor a cluster of GPUs. They want to set up alerts for when GPUs are running at high temperatures for extended periods. Which DCGM feature should they use?

Accepted Answer

DCGM health checks with custom thresholds and alerting via DCGM exporter to Prometheus.. DCGM health checks allow defining thresholds for GPU metrics, including temperature. When combined with the DCGM exporter and Prometheus, teams can create alerting rules that trigger on sustained high temperatures. This is the recommended method for cluster-wide monitoring. Other options either misuse DCGM diagnostics, assume non-existent features, or use less scalable methods.

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DCGM's built-in email alerting system configured via dcgm.conf.

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DCGM diagnostics with the '-r' flag to run a comprehensive test and report temperature violations.

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NVIDIA-smi's '--query-gpu=temperature.gpu' with a cron job to check and send alerts.

Question 10

An administrator is deploying NVIDIA AI Enterprise on a Kubernetes cluster and wants to verify that the GPU Operator has successfully installed all required components. Which command should the administrator use to check the status of the GPU Operator pods?

Accepted Answer

kubectl get pods -n gpu-operator. The GPU Operator installs its components as pods in the gpu-operator namespace. Checking pod status with kubectl get pods -n gpu-operator is the correct way to verify that all required components are running. Other commands provide GPU-level or node-level information but do not show the status of the operator's pods.

Answer

nvidia-smi -q

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kubectl get nodes -o wide

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dcgmi discovery -l