100% Free Real Updated NCP-AII Questions & Answers Pass Your Exam Easily [Q55-Q70]

NO.55 Which of the following are key benefits of using NVIDIA NVLink Switch in a multi-GPU server setup for AI and deep learning workloads?

Increased GPU-to-GPIJ communication bandwidth.

Reduced latency in inter-GPU data transfers.

Simplified GPU resource management.

Support for larger GPU memory pools than a single server can physically accommodate.

Enhanced security features compared to PCle based interconnections.

NO.56 A distributed training job using multiple nodes, each with eight NVIDIA GPUs, experiences significant performance degradation. You notice that the network bandwidth between nodes is consistently near its maximum capacity. However, ‘nvidia-smi’ shows low GPU utilization on some nodes. What is the MOST likely cause?

The GPUs are overheating, causing thermal throttling.

Data is not being distributed evenly across the nodes; some nodes are waiting for data from others.

The NVIDIA drivers are outdated, causing communication bottlenecks.

The network interface cards (NICs) are faulty, causing packet loss and retransmissions.

The CPU is heavily loaded, causing contention for network resources.

NO.57 You’ve replaced a faulty NVIDIA Quadro RTX 8000 GPU with an identical model in a workstation. The system boots, and ‘nvidia-smi’ recognizes the new GPU. However, when rendering complex 3D scenes in Maya, you observe significantly lower performance compared to before the replacement. Profiling with the NVIDIA Nsight Graphics debugger shows that the GPU is only utilizing a small fraction of its available memory bandwidth. What are the TWO most likely contributing factors?

The new GPU’s PCle link speed is operating at a lower generation (e.g., Gen3 instead of Gen4).

The NVIDIA OptiX denoiser is not properly configured or enabled.

The workstation’s power plan is set to ‘Power Saver,’ limiting GPU performance.

The Maya scene file contains corrupted or inefficient geometry.

The newly installed GPU’s VBIOS has not been properly flashed, causing an incompatibility issue.

NO.58 A server with four installed NVIDIA GPUs is experiencing intermittent crashes during heavy AI training workloads. You suspect a power issue. You have monitored the power consumption and found that the GPUs are briefly exceeding the rated power capacity of the PSU during peak loads. What are TWO effective mitigation strategies you can implement? (Select TWO)

Underclock the GPIJs to reduce their power consumption.

Replace the PSU with a higher wattage PSU.

Disable one of the GPUs to reduce the total power draw.

Increase the server room temperature.

Re-seat the GPUs in their respective slots.

NO.59 Which protocol is commonly used in Spine-Leaf architectures for dynamic routing and load balancing across multiple paths?

STP (Spanning Tree Protocol)

OSPF (Open Shortest Path First)

VRRP (Virtual Router Redundancy Protocol)

ECMP (Equal-Cost Multi-Path)

BGP (Border Gateway Protocol)

NO.60 You are using the NVIDIA Container Toolkit in a Kubernetes environment with multiple GPUs per node. You want to ensure that pods can request specific GPUs on a node, rather than simply requesting ‘any’ GPU. Which Kubernetes feature, in conjunction with the NVIDIA Device Plugin, allows you to achieve this fine-grained GPU resource allocation?

Resource Quotas

Node Affinity

Topology Manager

Device Plugins API

Taints and Tolerations

NO.61 A user reports that their CUDA application is running slower than expected after an NVIDIA driver update. You suspect a driver compatibility issue. How can you revert to a previous NVIDIA driver version on an Ubuntu system, assuming you have the older driver package ‘nvidia-driver-470 470.82.00-0ubuntu1_amd64.deb’?

Simply install the .deb’ package using ‘sudo dpkg -i

First, remove the current NVIDIA driver using ‘sudo apt purge nvidia- s, then install the .deb’ package using ‘sudo dpkg -i nvidia-driver-470_470.82.00-Oubuntu1_amd64.deb’ and resolve any dependency issues with ‘sudo apt –fix-broken install’.

Use the NVIDIA System Management Interface Cnvidia-smi’) to downgrade the driver.

Edit the ‘/etc/apt/sources.list’ file to point to an older repository containing the desired driver version, then update and upgrade the system.

Install the older .run’ installer version, if you previously installed it using .run’, using the -uninstall option first.

NO.62 You are troubleshooting a network performance issue in your NCP-AII environment. After running ‘ibstat’ on a host, you see the following output for one of the InfiniBand ports:

What does the ‘LMC: 0’ indicate, and what are the implications for network performance?

LMC: 0 indicates that the link is down and not functioning correctly.

LMC: 0 indicates that Link Aggregation (LAG) is not enabled on this port, meaning only a single link is being used for communication.

LMC : 0 indicates the port is operating at the lowest possible speed.

LMC : 0 indicates that the Subnet Manager is not running correctly.

LMC : 0 is the default and expected value; it has no impact on performance.

NO.63 You’re deploying a BlueField-2 DPU in a cloud environment and need to ensure the integrity of the DPU’s firmware. You want to verify that the firmware hasn’t been tampered with. Which of the following methods provides the strongest level of assurance for firmware integrity?

Checking the MD5 checksum of the firmware image against a known good value.

Verifying the SHA256 checksum of the firmware image against a known good value provided by NVIDIA.

Comparing the firmware version reported by the DPU with the version listed in the NVIDIA release notes.

Using a digitally signed firmware image and verifying the signature using NVIDIA’s public key.

Checking the file size of the firmware image against a known good value.

NO.64 You are attempting to install NGC CLI on a CentOS 7 system, but the ‘pip install nvidia-cli’ command fails with a ‘Could not find a version that satisfies the requirement nvidia-cli’ error. You have confirmed that ‘pip’ is installed and working. What could be the cause of this issue?

The CentOS 7 system does not have the required Python version installed. NGC CLI requires Python 3.6 or later.

The system’s package manager (YUM) is not configured correctly, preventing ‘pip’ from finding the NGC CLI package.

The ‘pip’ version is outdated and incompatible with the NGC CLI package. Upgrade ‘pip’ using ‘pip install -upgrade pip’.

The system’s firewall is blocking access to the Python Package Index (PyPl). CentOS 7 is not supported by NGC CLI.

NO.65 You are deploying an NVIDIA GPU-accelerated application in a virtualized environment using vGPU. How does vGPU technology impact power and cooling considerations compared to a bare-metal deployment, and what specific monitoring metrics become crucial?

vGPU deployments typically require less power and cooling than bare-metal, as resources are shared. The host server’s overall power consumption becomes the primary monitoring metric.

vGPU deployments have no significant impact on power and cooling requirements compared to bare-metal. Standard GPU temperature and power draw metrics are sufficient.

vGPU deployments can lead to higher overall power consumption and concentrated heat generation on the host server due to resource consolidation. Monitoring metrics like GPU utilization per VM, vGPU frame rate, and host server thermal headroom become crucial.

vGPU deployments eliminate the need for GPU monitoring, as the virtualization layer handles all power and cooling management.

vGPU deployments require specialized cooling solutions that are not needed for bare metal setups.

NO.66 You have created MIG instances on an A100 GPU and want to dynamically adjust their size based on workload demands. Which of the following methods is the most appropriate for automatically resizing MIG instances in response to changing resource requirements?

Use ‘nvidia-smi’ to manually destroy and recreate MIG instances with different sizes as needed.

Implement a script that monitors GPU utilization and automatically adjusts Kubernetes resource quotas to match.

Leverage a GPU virtualization platform with dynamic resource allocation capabilities that integrates with MIG.

Utilize CUDA MPS to dynamically allocate GPU resources to different processes.

Adjust the application code to use less GPIJ memory dynamically.

NO.67 You’re deploying a multi-GPU training job on a cluster using Slurm. You need to ensure that the GPUs allocated to the job are healthy and functioning correctly before the training starts. What’s the MOST effective approach to pre-validate the GPU hardware?

Run a simple CUDA vector addition program on each GPU and check for errors.

Check the output of ‘nvidia-smi’ to ensure all GPUs are listed and have the expected memory.

Execute the NVIDIA Data Center GPU Manager (DCGM) diagnostic suite on the allocated GPUs.

Monitor the GPU temperature using ‘nvidia-smi’ during the first few minutes of the training job.

Allocate all available GPUs to the job and assume they are healthy.

NO.68 An A1 server exhibits frequent kernel panics under heavy GPU load. ‘dmesg’ reveals the following error: ‘NVRM: Xid (PCl:0000:3B:00): 79, pid=…, name=…, GPU has fallen off the bus.’ Which of the following is the least likely cause of this issue?

Insufficient power supply to the GPIJ, causing it to become unstable under load.

A loose or damaged PCle riser cable connecting the GPU to the motherboard.

A driver bug in the NVIDIA drivers, leading to GPU instability.

Overclocking the GPU beyond its stable limits.

A faulty CPU.

NO.69 You have installed an NVIDIA ConnectX-7 network adapter in an A1 server and configured RDMA over Converged Ethernet (RoCE). During validation, you observe very high latency between two servers communicating over RoCE. Which of the following are potential causes? (Choose two)

Incorrect MTU size configuration on the network interfaces.

The network switch does not support RoCE.

The network cables are damaged.

The GPU driver is outdated.

Insufficient memory on the network adapter.

NO.70 You are using MIG (Multi-lnstance GPU) on an NVIDIAAI 00 GPU within a Kubernetes cluster. You want to configure a pod to use a specific MIG instance. How do you define the GPU resource request in the pod’s YAML definition?

Specify the full GPU ID (e.g., ‘nvidia.com/gpu: 1’) in the resource request.

Specify the MIG device profile name (e.g., ‘nvidia.com/mig-l g.7gb: 1 in the resource request.

Use a generic resource request like ‘nvidia.com/gpu: 0.125’ to allocate a portion of the GPIJ.

Use a custom resource definition (CRD) to define MIG instances and reference it in the resource request.

Using the annotation ‘nvidia.com/gpu.product’ and the value of the particular GPU device you desire.