NCP-AIO Installation and Deployment Practice Test 5 — 15 Questions

Question 1

An administrator is deploying NVIDIA AI Enterprise on a bare-metal cluster. Which component is mandatory to provide the necessary abstraction layer for containerized GPU resources?

Accepted Answer

NVIDIA GPU Operator. The NVIDIA GPU Operator is essential for automating the management of all NVIDIA software components needed to provision Kubernetes with GPU support. It manages the lifecycle of drivers, container runtimes, and monitoring tools. By leveraging the Operator, administrators ensure consistent configuration across the cluster, preventing drift and ensuring that CUDA workloads have the correct dependencies to execute reliably on bare-metal infrastructure.

Answer

NVIDIA Triton Inference Server

Answer

NVIDIA Base Command

Answer

NVIDIA CUDA Toolkit

Question 2

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.

Answer

Provisioning virtual machines

Answer

Optimizing neural network layers

Answer

Managing Kubernetes network policies

Question 3

Which file format is commonly used to define the configuration and state for the NVIDIA GPU Operator within a Kubernetes environment?

Accepted Answer

YAML. Kubernetes uses YAML to define declarative states for resources, including Custom Resource Definitions (CRDs) used by operators. The GPU Operator leverages YAML manifests to configure settings such as driver version, toolkit installation, and monitoring agents. Understanding this format is vital for AI Ops professionals, as it allows for version-controlled infrastructure-as-code practices, ensuring deployments are reproducible and auditable across various environments.

Answer

JSON

Answer

TOML

Answer

XML

Question 4

Which NVIDIA software component is responsible for providing the necessary CUDA libraries to containerized applications?

Accepted Answer

NVIDIA Container Toolkit. The NVIDIA Container Toolkit is the critical bridge. It provides the necessary libraries and container runtime hooks that allow processes inside a container to access the host's GPU and CUDA environment. This setup is fundamental for AI Enterprise, as it enables portability of AI applications while maintaining high-performance access to physical GPU hardware, regardless of the underlying host OS distribution or container runtime used.

Answer

NVIDIA Driver

Answer

NVIDIA vGPU Manager

Answer

NVIDIA Triton

Question 5

What is the primary benefit of using an NVIDIA-Certified System for AI Enterprise deployments?

Accepted Answer

Validated compatibility and performance. NVIDIA-Certified Systems have passed rigorous testing to ensure optimal performance and compatibility with the NVIDIA AI Enterprise software stack. This certification minimizes the risk of hardware-software conflicts, performance bottlenecks, and stability issues during production. For AI Ops professionals, this translates to faster deployment times, lower maintenance costs, and high confidence in the reliability of the underlying infrastructure for mission-critical deep learning and inference workloads.

Answer

Guaranteed 100% network uptime

Answer

Automatic software license renewal

Answer

Free access to all AI models

Question 6

What is the primary role of a private container registry in an NVIDIA AI Enterprise deployment?

Accepted Answer

Storing and distributing container images. A private registry provides a secure, reliable, and high-speed source for container images within the enterprise firewall. By caching NVIDIA-provided containers locally, organizations avoid dependency on public repositories, which is critical for security compliance and offline operational readiness. It ensures that the cluster has consistent, immutable versions of software available, preventing drift and ensuring that deployments remain stable and reproducible across the entire production environment.

Answer

Managing hardware firmware updates

Answer

Monitoring real-time GPU thermals

Answer

Providing GPU driver licensing keys

Question 7

When installing the NVIDIA GPU Operator, which namespace is typically used to ensure proper isolation and role-based access control?

Accepted Answer

gpu-operator. Using a dedicated namespace like 'gpu-operator' is a best practice in Kubernetes. It isolates the operator's resources, permissions, and lifecycle from other cluster services. This separation allows for granular security policies, making it easier to manage access and ensuring that only authorized personnel can modify the operator's configuration, which is vital for maintaining the security and integrity of the GPU-enabled infrastructure in a production environment.

Answer

default

Answer

kube-system

Answer

public-apps

Question 8

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.

Answer

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

Answer

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

Answer

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

Question 9

Which tool is the industry standard for monitoring and managing NVIDIA data center GPUs in a large-scale cluster deployment?

Accepted Answer

NVIDIA DCGM. The NVIDIA Data Center GPU Manager (DCGM) provides a comprehensive set of APIs and tools for managing and monitoring GPUs in production environments. It is essential for AI operations because it allows for real-time health checks, diagnostic testing, and policy-based management of GPU resources across a distributed cluster, ensuring high availability and identifying performance bottlenecks before they impact critical training or inference workflows.

Answer

nvidia-smi

Answer

CUDA Debugger

Answer

NVIDIA Triton Inference Server

Question 10

Which TWO of the following are prerequisites for installing the NVIDIA Container Toolkit on a Linux host?

Accepted Answer

A compatible NVIDIA driver already installed on the host.. To successfully deploy the NVIDIA Container Toolkit, the host must have a functional NVIDIA driver and a container runtime like Docker or containerd. These prerequisites ensure that the runtime has a target to interface with and can correctly map host-side GPU resources into the container namespace. Without these core components, the toolkit cannot bridge the gap between physical hardware and isolated containerized processes.

Answer

The latest version of the CUDA Toolkit installed in every container.

Answer

An active subscription to NVIDIA AI Enterprise.

Answer

A pre-configured Kubernetes cluster with Helm.

Question 11

When configuring the NVIDIA Device Plugin for Kubernetes, what is the purpose of the 'time-slicing' configuration?

Accepted Answer

To allow multiple pods to share a single physical GPU through context switching.. Time-slicing is a technique that allows multiple Kubernetes pods to share a single GPU by rapidly switching context between them. In scenarios where full GPU isolation is not required, this increases resource utilization. It is a vital deployment strategy for optimizing cost and efficiency in shared environments, allowing administrators to balance the workload across limited GPU hardware without needing more expensive virtual machine-based partitioning solutions.

Answer

To schedule tasks based on the specific time of day for load balancing.

Answer

To increase the GPU's clock frequency during high-demand periods.

Answer

To restrict access to the GPU based on container process priority.

Question 12

Which action must be performed after updating the NVIDIA driver on a Linux host to ensure that all active GPU containers recognize the new driver version?

Accepted Answer

Restart the containerized applications.. Containers utilize the host-level NVIDIA drivers injected via the container toolkit. When the host driver is updated, active containers will still be referencing the old driver libraries in their environment until they are restarted. Restarting the containers is the mandatory step to force them to bind against the newly installed libraries, ensuring compatibility and preventing runtime errors associated with outdated driver interfaces.

Answer

Rebuild all container images with the latest CUDA toolkit.

Answer

Upgrade the Docker daemon version.

Answer

Re-run the nvidia-container-toolkit installation script.

Question 13

Which THREE of the following are benefits of using Multi-Instance GPU (MIG) technology in a Kubernetes environment?

Accepted Answer

Hardware-level isolation between GPU workloads.. MIG allows for hardware-level isolation, ensuring that one workload's memory usage or compute spikes do not impact others on the same physical GPU. This provides deterministic Quality of Service (QoS), which is critical for multi-tenant AI environments. By partitioning resources, administrators can optimize GPU allocation, allowing smaller, less intensive tasks to run on a fraction of the GPU, thereby significantly increasing overall cluster efficiency.

Answer

Increased total GPU count available to the OS kernel.

Answer

Automatic translation of CUDA code to work on different GPU architectures.

Question 14

When installing NVIDIA drivers via a package manager, what is the importance of the 'dkms' package?

Accepted Answer

It ensures the driver module is rebuilt automatically during kernel upgrades.. DKMS (Dynamic Kernel Module Support) is critical because it automates the recompilation of the NVIDIA kernel module whenever a new Linux kernel is installed. In AI production environments, kernel updates are common for security and stability. DKMS ensures that the GPU remains functional after an update without requiring manual driver intervention, preventing unplanned downtime for AI workloads and simplifying system administration tasks.

Answer

It provides the graphical user interface for managing GPU settings.

Answer

It improves the performance of the CUDA compiler during the build process.

Answer

It manages the firmware updates for the GPU hardware.

Question 15

Which configuration file is typically modified to enable the NVIDIA Device Plugin in a Kubernetes cluster?

Accepted Answer

A Kubernetes DaemonSet manifest (YAML).. The NVIDIA Device Plugin is typically deployed as a DaemonSet using a YAML manifest. Modifying this manifest allows administrators to customize settings like the MIG strategy, time-slicing, and resource allocation policies. Correct configuration of this file is essential for ensuring that the Kubernetes scheduler correctly recognizes and assigns GPU resources to pods, which is the cornerstone of effective AI cluster management in modern cloud-native environments.

Answer

/etc/docker/daemon.json

Answer

/etc/nvidia/nvidia-container-runtime.json

Answer

/etc/kubernetes/kubelet.conf