NCP-AIO Installation and Deployment Practice Test 4 — 15 Questions

Question 1

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.

Answer

NVIDIA CUDA Toolkit.

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NVIDIA Container Toolkit.

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NVIDIA Deep Learning GPU Training System (DIGITS).

Question 2

When deploying NVIDIA AI Enterprise, why is the selection of the correct CUDA version in the container image critical during the installation phase?

Accepted Answer

The CUDA version must be compatible with the host driver version.. The CUDA version dictates which APIs and features are available to the AI application. Because the driver on the host must support the CUDA version used by the container (backward compatibility), mismatching these leads to runtime failures. This is a crucial AI Ops consideration as it directly affects the stability of the entire stack, ensuring that the software environment aligns with the underlying hardware capabilities for maximum performance and reliability.

Answer

It determines the speed of the GPU's memory bus.

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It enables the use of the NVIDIA License System.

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It is required for the installation of the GPU Operator.

Question 3

Refer to the exhibit. The administrator has deployed the GPU Operator, but the node does not show GPU resources. What is the most likely cause?

Accepted Answer

The NFD service is not installed or configured correctly.. The lack of GPU resources in the node description indicates that the Node Feature Discovery (NFD) or the GPU Operator has not correctly identified or labeled the GPU hardware. This is a common deployment issue where the hardware-to-software handshake fails due to missing labels. Identifying this early is key to ensuring that the Kubernetes scheduler can correctly place AI workloads on nodes equipped with the necessary compute resources.

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The node is not part of a valid Kubernetes namespace.

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The GPU firmware is out of date.

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The cluster is using an unsupported CPU architecture.

Question 4

During an NVIDIA AI Enterprise deployment, you are asked to configure the 'NVIDIA Container Toolkit'. What is its primary function?

Accepted Answer

To allow the container runtime to interact with the host's GPU.. The Container Toolkit enables containers to access the GPU by exposing the necessary drivers, libraries, and device files from the host into the container runtime. It acts as the critical bridge between the hardware-level drivers and the containerized applications. Understanding this is foundational for AI Ops, as it ensures that containerized AI models can actually utilize the GPU hardware for training or inference tasks without manual configuration.

Answer

To provide high-level APIs for neural network training.

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To act as a package manager for AI model weight files.

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To automatically optimize neural network hyper-parameters.

Question 5

Which of the following describes the purpose of the NVIDIA GPU Operator's 'Driver Container'?

Accepted Answer

To provide a platform-agnostic way to deploy NVIDIA drivers.. The Driver Container is a critical component that builds or pulls the correct driver for the specific host OS and kernel. It automates the complex process of driver installation, ensuring compatibility across heterogeneous node environments. By containerizing the driver, the Operator simplifies maintenance and upgrades, reducing the risk of configuration drift and ensuring that nodes always have a functional driver compatible with the latest AI software releases.

Answer

To store the persistent state of trained neural networks.

Answer

To manage the licensing of the AI Enterprise suite.

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To act as a gateway for remote GPU access.

Question 6

In an air-gapped environment, what must an administrator do to ensure the GPU Operator correctly installs the necessary software components?

Accepted Answer

Configure the GPU Operator to use a local image registry.. In an air-gapped environment, the cluster cannot reach external repositories (e.g., NGC or Docker Hub). The administrator must pre-populate a local container registry with all required images and update the Operator configuration to point to this local registry. This is a common enterprise task for secure environments that prevents deployment failures caused by connection timeouts or authentication issues when accessing public NVIDIA software mirrors.

Answer

Enable the 'offline-mode' flag in the Kubernetes API.

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Manually copy the VIB files to every node.

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Disable the validation of container image signatures.

Question 7

Which NVIDIA technology enables the partitioning of a single physical GPU into multiple independent instances for use by different virtual machines or containers?

Accepted Answer

NVIDIA Multi-Instance GPU (MIG).. NVIDIA Multi-Instance GPU (MIG) technology is the correct answer. It allows a single GPU to be securely partitioned at the hardware level, providing guaranteed QoS and isolation for different workloads. This is essential for maximizing GPU utilization in enterprise AI, as it enables the co-location of small inference tasks alongside larger training workloads on a single piece of high-end hardware.

Answer

NVIDIA GPUDirect Storage.

Answer

NVIDIA NVLink.

Answer

NVIDIA vGPU Profiles.

Question 8

You are troubleshooting a node where the GPU is detected, but the application fails to utilize it. Which log source would provide the most relevant information?

Accepted Answer

The NVIDIA container runtime logs.. The NVIDIA container runtime logs and the application-level logs are the most important sources. If the GPU is visible to the system but not the application, the issue is likely a driver/runtime mismatch or a library path configuration. Checking these logs allows an administrator to isolate whether the fault is in the container orchestration layer or the application's software environment, which is vital for rapid resolution in production environments.

Answer

The BIOS system event log.

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The cluster's physical network switch logs.

Answer

The local NTP synchronization logs.

Question 9

Which NVIDIA tool allows you to verify that the GPU and its driver are properly installed and functioning on a Linux system?

Accepted Answer

nvidia-smi. The 'nvidia-smi' (System Management Interface) tool is the standard utility for interacting with the NVIDIA driver. It provides a real-time status of GPU utilization, temperature, memory usage, and driver versions. Being proficient with this tool is essential for an AI Ops professional to quickly validate hardware health, confirm driver installation, and identify if a GPU is accessible by the host OS after a fresh installation or reboot.

Answer

nvcc --version

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lspci | grep nvidia

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docker run --gpus all

Question 10

An AI engineer is deploying a large language model on an NVIDIA DGX system. The deployment fails with an error indicating an insufficient NVIDIA driver version for the required CUDA toolkit. Which action should the engineer take to resolve the dependency mismatch?

Accepted Answer

Upgrade the NVIDIA driver to a version verified as compatible with the required CUDA toolkit.. Drivers and CUDA versions maintain a strict compatibility matrix. Installing the latest driver version supported by the specific DGX OS is essential to ensure the CUDA runtime can interface correctly with the GPU hardware. This task is critical in production environments because mismatched drivers lead to kernel panics or silent performance degradation, directly impacting the availability of AI workloads running on the cluster infrastructure.

Answer

Reinstall the CUDA toolkit using a generic installer without checking the NVIDIA driver version compatibility matrix.

Answer

Downgrade the OS kernel to a legacy version to force compatibility with an older CUDA toolkit.

Answer

Modify the LD_LIBRARY_PATH environment variable to prioritize older CUDA libraries found on the system.

Question 11

Refer to the exhibit. An AI engineer observes that a model training job is running slower than expected. Based on the output, what is the primary cause of the performance degradation?

Accepted Answer

An administrator has set a software power limit that is lower than the GPU's maximum performance threshold.. The 'SW Power Cap' throttle reason indicates that the power limit is configured below the card's maximum design capacity, causing the GPU to downclock to P12 state to stay within the power envelope. This is crucial for AI operations because it signals that the hardware is being artificially throttled, necessitating a review of power policies to ensure optimal training performance in high-compute scenarios.

Answer

The GPU is overheating and the thermal management system has engaged to prevent hardware damage.

Answer

The GPU is currently idle and the driver has shifted the card to an energy-saving state.

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The GPU is experiencing a PCIe bus error, forcing the system to reduce clock speeds to maintain stability.

Question 12

When installing the NVIDIA Container Toolkit to enable GPU acceleration in Docker, which file must be modified or verified to ensure the container runtime can access the NVIDIA runtime?

Accepted Answer

/etc/docker/daemon.json. The daemon configuration file, typically located at /etc/docker/daemon.json, must be updated to include the NVIDIA runtime. This integration is the foundational step for AI containerization, as it allows the Docker engine to map host GPU resources into the container namespace. Without this configuration, containers will fail to detect GPUs, rendering them incapable of running accelerated AI applications.

Answer

/etc/fstab

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/etc/environment

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/boot/grub/grub.cfg

Question 13

Which utility is primarily used to monitor and manage NVIDIA GPU power, temperature, and usage statistics in real-time on a Linux-based deployment?

Accepted Answer

nvidia-smi. nvidia-smi (NVIDIA System Management Interface) is the command-line utility used to interact with the NVIDIA driver for monitoring and managing GPU devices. It is the fundamental tool for AI ops teams to verify that GPUs are properly detected and performing within expected thermal and power envelopes during model training or inference tasks on DGX or workstation systems.

Answer

top

Answer

htop

Answer

dmesg

Question 14

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.

Answer

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

Answer

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

Answer

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

Question 15

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.

Answer

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.