NCP-AIO Practice Test 2 — 25 Questions

Question 1

What is the primary function of an 'InitContainer' in an NVIDIA GPU-enabled pod deployment?

Accepted Answer

To ensure prerequisites are satisfied before the main application starts.. InitContainers are often used to ensure that environment-specific requirements—such as configuring GPU drivers, validating libraries, or mounting persistent volumes—are met before the main training application starts. In AI workloads, this is critical for ensuring that dependencies are correctly loaded or data is staged, preventing runtime failures that would occur if the main training process attempted to execute in an incomplete environment. This pattern enhances the robustness of automated AI workflows.

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To serve as a secondary GPU compute thread during training.

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To permanently store the model weights after training.

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To bypass GPU scheduling constraints and force-load the job.

Question 2

An AI operations engineer is troubleshooting an inference service on an NVIDIA A100 GPU that shows intermittent stalls. The monitoring dashboard reports GPU utilization at 100%, but request throughput is far below the validated baseline. Running nvidia-smi dmon reveals the SM utilization is high while memory controller utilization is low. Which action should the engineer take first to identify the bottleneck?

Accepted Answer

Profile the inference process with Nsight Systems to inspect kernel execution and identify serialization gaps.. The high SM utilization with low memory controller utilization suggests the GPU is busy but not doing useful work, often due to kernel serialization or CPU-GPU synchronization stalls. Profiling with Nsight Systems provides the timeline needed to see gaps and serialization. Only after identifying the specific stall should configuration changes be considered, making profiling the correct first step.

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Switch the inference precision from FP16 to FP32 to improve numerical stability.

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Enable MPS (Multi-Process Service) to allow concurrent kernel execution from multiple processes.

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Increase the batch size in the inference server configuration to improve GPU occupancy.

Question 3

What is the primary function of the NVIDIA Persistence Daemon in an AI deployment?

Accepted Answer

It keeps the GPU driver initialized to reduce latency during application startup.. The Persistence Daemon ensures that the NVIDIA driver remains loaded even when no applications are using the GPU. This prevents the driver from unloading and then reloading when a job starts, which significantly reduces the startup latency of AI models. It is a critical configuration for high-performance environments where frequent job scheduling would otherwise incur unnecessary overhead from repeated driver and device initialization.

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It automatically updates the NVIDIA driver when a new version is released on the web.

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It monitors GPU health and automatically initiates a reboot if an error is detected.

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It manages the network traffic between the GPU and the storage backend.

Question 4

A financial services firm must prove to auditors that an AI training job ran on hardware located only in its Frankfurt data center and that no pod could ever be scheduled onto GPUs in other regions. The cluster spans three regions with nodes labeled topology.kubernetes.io/region. Which approach most directly enforces this placement requirement?

Accepted Answer

Define a nodeAffinity rule in the pod spec requiring topology.kubernetes.io/region to equal eu-central-1, and mark it requiredDuringSchedulingIgnoredDuringExecution.. Hard placement guarantees come from required node affinity, which makes matching labels a precondition for scheduling. Because the scheduler will not place the pod on any node whose region label differs, the job cannot leave the Frankfurt data center, giving the firm an enforceable, auditable control over GPU location.

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Add a preferredDuringSchedulingIgnoredDuringExecution nodeAffinity rule that favors the Frankfurt region with a high weight.

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Set the pod's restartPolicy to Never and add a toleration for the region-specific taint on Frankfurt nodes.

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Create a PodDisruptionBudget for the training job that limits voluntary evictions to zero during the run.

Question 5

An administrator needs to ensure that all GPU drivers are updated across a heterogeneous cluster without causing downtime. What is the best strategy?

Accepted Answer

Perform rolling updates by draining nodes one by one. Implementing a rolling update strategy allows the cluster to be updated one node at a time while the remaining nodes continue to process training jobs. By draining the target node of all active jobs, upgrading the drivers, and then re-adding the node to the production pool, the administrator maintains service availability. This is the standard practice in production AI operations to minimize disruption and ensure consistent driver versions across the entire fleet.

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Simultaneously update all nodes during a maintenance window

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Use a containerized driver approach to avoid host updates

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Only update the driver on the master node

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.

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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.

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

Question 7

When configuring a node for NVIDIA AI Enterprise in a Kubernetes environment, what is the primary function of the NVIDIA Container Toolkit?

Accepted Answer

Exposing GPUs to containers. The NVIDIA Container Toolkit allows container engines, such as Docker or containerd, to interface with physical GPUs. It enables the exposure of GPUs inside containers, ensuring that applications can access CUDA libraries and hardware acceleration. Without this toolkit, the container runtime cannot bridge the gap between the host's GPU hardware and the containerized process, effectively rendering the GPU unusable for AI workloads.

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Provisioning virtual machines

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Optimizing neural network layers

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Managing Kubernetes network policies

Question 8

A platform team runs an NVIDIA AI cluster with the GPU Operator deployed. Users submit jobs directly with kubectl and frequently request whole GPUs even when their notebooks only need a fraction of one. The team wants Kubernetes itself to admit and queue jobs based on GPU demand without users changing their manifests. Which component should the team deploy to meet this requirement?

Accepted Answer

NVIDIA KAI Scheduler. The requirement is for Kubernetes to make admission and queueing decisions based on GPU demand while users submit normal manifests. KAI Scheduler is the NVIDIA component that provides queue-based, gang-aware scheduling for AI workloads and understands fractional GPU requests, so jobs wait rather than fail. The GPU Operator, MIG Manager, and DCGM Exporter each address installation, partitioning, or observability rather than scheduling admission.

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NVIDIA MIG Manager configured for every GPU

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NVIDIA GPU Operator with the device plugin enabled

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NVIDIA DCGM Exporter with Prometheus alerting

Question 9

Which strategy is most effective for managing heterogeneous GPU clusters containing both older architectures (e.g., V100) and newer architectures (e.g., H100)?

Accepted Answer

Using Node Selectors and Taints/Tolerations.. Using Kubernetes Node Selectors and Taints/Tolerations allows administrators to route specific workloads to compatible hardware. This is essential because newer GPUs support features like MIG or specific precision formats that older hardware lacks. By properly tagging nodes based on architecture, AI Ops prevents 'Incompatible Device' errors and ensures that high-performance training jobs are scheduled on hardware that meets their specific computational requirements, optimizing cluster-wide performance and efficiency.

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Enabling uniform scheduling across all nodes.

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Configuring the NVIDIA Device Plugin to hide older GPUs.

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Deploying a single unified container image for all jobs.

Question 10

A platform team is preparing a bare-metal Kubernetes cluster to run AI workloads. They want the GPU Operator to install and manage the NVIDIA driver automatically on each node. Which prerequisite must be satisfied on every worker node before the GPU Operator can succeed?

Accepted Answer

The node must have a supported Linux kernel with matching kernel headers available for the driver container to build against.. The driver container builds the NVIDIA kernel module at runtime against the host kernel, so matching kernel headers or development packages must be present on each node. The GPU Operator manages the container toolkit, DCGM, and device plugin itself, so those do not need to be preinstalled. vGPU Manager applies to virtualized deployments, not bare-metal driver automation.

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The node must have the NVIDIA Container Toolkit preinstalled and configured.

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The node must be running the NVIDIA Data Center GPU Manager (DCGM) as a systemd service before joining the cluster.

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The node must have the NVIDIA vGPU Manager installed so the physical GPU can be partitioned before driver installation.

Question 11

A DevOps engineer needs to monitor GPU health metrics in real-time for workload management. Which tool provides the most granular visibility into GPU utilization and power consumption for individual containers?

Accepted Answer

NVIDIA DCGM Exporter.. The NVIDIA DCGM (Data Center GPU Manager) Exporter is the industry-standard tool for collecting fine-grained GPU telemetry. By integrating with Prometheus and Grafana, it allows administrators to track metrics like power usage, temperature, and utilization at the container level. This granular data is vital for proactive workload management, allowing for autoscaling based on actual hardware demand rather than generic CPU metrics.

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Kubernetes Horizontal Pod Autoscaler (HPA) with CPU metrics.

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Standard Linux 'top' command.

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Docker stats command.

Question 12

When configuring a multi-tenant environment on an NVIDIA DGX system, how are MIG (Multi-Instance GPU) instances best provisioned?

Accepted Answer

Using the nvidia-smi command to define the desired MIG profiles.. MIG allows a single GPU to be partitioned into multiple isolated instances, each with dedicated compute and memory resources. Configuring MIG via the nvidia-smi tool at the OS level ensures that these partitions are visible to the container runtime. This is crucial for AI operations, as it enables safe, secure multi-tenancy where different users can run jobs simultaneously without resource contention or cross-tenant interference.

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Through the BIOS settings, creating physical partitions on the GPU die.

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By editing the /etc/nvidia/mig-config.json file directly.

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Via the OS kernel boot parameters in the GRUB configuration.

Question 13

A Kubernetes cluster running the NVIDIA GPU Operator is shared by an inference team and a research team. The research team's training pods repeatedly evict the inference pods from GPUs, causing latency spikes in production. The administrator wants to guarantee that inference pods always get GPU access first. Which Kubernetes scheduling mechanism should be configured?

Accepted Answer

Assign a higher PriorityClass to the inference pods and enable preemption on the scheduler.. Priority and preemption are the native Kubernetes controls that decide which pods win when GPU resources are contended. Giving inference pods a higher PriorityClass lets the scheduler admit them first and evict lower-priority training pods when necessary, which directly satisfies the requirement that production inference always obtains GPU access.

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Enable the NVIDIA MIG feature on all GPUs and dedicate a MIG instance to each inference pod.

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Configure a taint on the GPU nodes and add the corresponding toleration only to the inference pods.

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Create a PodDisruptionBudget for the inference deployment and set maxUnavailable to zero.

Question 14

Refer to the exhibit. The system has two GPUs. What is the most likely cause of the observed performance discrepancy?

Accepted Answer

Only one GPU is receiving the workload due to improper data distribution.. The exhibit shows one GPU heavily utilized while the second is idling despite similar memory consumption. This indicates a data parallelism imbalance, where one process is doing the bulk of the work. This is a common issue in multi-GPU setups where workload distribution is not correctly handled, leading to massive inefficiencies where expensive hardware is under-utilized, significantly increasing the time required for model training or inference tasks.

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The GPUs have different amounts of physical memory.

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The system is limited by the PCIe bus bandwidth.

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The model is too small to be parallelized across two GPUs.

Question 15

Refer to the exhibit. The training job is showing intermittent "thermal throttling" warnings. Which configuration change is the most appropriate adjustment?

Accepted Answer

Lower the "power_limit_watts" value.. Thermal throttling occurs when the GPU reaches its maximum operating temperature and lowers its clock speed to prevent physical damage. While power limits can be adjusted to reduce heat, simply lowering the limit may negatively impact training performance. Adjusting the power limit to a value that balances thermal overhead with compute requirements is a necessary trade-off to ensure stable, consistent performance during long training runs without hardware damage.

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Set "compute_mode" to "exclusive_process".

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Disable ECC mode to lower the power consumption.

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Set "persistence" to "disabled".

Question 16

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 17

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 18

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 19

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 20

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