Authentic NCP-AII Dumps – Free PDF Questions to Pass [Q98-Q118]

Q98. You are replacing a faulty NVIDIA Tesla V 100 GPU in a server. After physically installing the new GPU, the system fails to recognize it. You’ve verified the power connections and seating of the card. Which of the following steps should you take next to troubleshoot the issue?

Immediately RMA the new GPU as it is likely defective.

Update the system BIOS and BMC firmware to the latest versions.

Reinstall the operating system to ensure proper driver installation.

Check if the new GPU requires a different driver version than the currently installed one and update if needed.

Disable and re-enable the GPU slot in the system BIOS.

Q99. Which of the following statements accurately describe the benefits of using MIG (Multi-lnstance GPU) in an AI/HPC environment?
(Select all that apply)

MIG allows a single GPU to be partitioned into multiple isolated instances, improving resource utilization.

MIG guarantees performance isolation between different workloads running on the same GPIJ.

MIG eliminates the need for GPU virtualization software.

MIG increases the overall FLOPS of the GPU.

MIG allows running multiple CUDA versions simultaneously on the same physical GPIJ.

Q100. You have a Kubernetes cluster with nodes running different versions of the NVIDIA driver. You need to ensure that your containerized AI applications are always compatible with the specific driver version running on the node where they are scheduled. How can you achieve this driver version compatibility in a cloud-native way?

Manually create different container images for each driver version and use node selectors to schedule the correct image on the appropriate nodes.

Use the NVIDIA driver capabilities to detect the driver version at runtime and dynamically load the correct libraries.

Use the NVIDIA Operator to automatically manage driver installations and updates on the nodes, ensuring a consistent driver version across the cluster.

Implement a webhook that inspects the node labels and injects the appropriate NVIDIA libraries into the pod at runtime.

Use a shared volume to mount drivers into a container.

Q101. You need to remotely monitor the GPU temperature and utilization of a server without installing any additional software on the server itself. Assuming you have network access to the server’s BMC (Baseboard Management Controller), which protocol and standard data format would BEST facilitate this?

SNMP (Simple Network Management Protocol) with MIB (Management Information Base)

HTTP with JSON

SSH with plain text output from ‘nvidia-smi’

IPMI (Intelligent Platform Management Interface) with SDR (Sensor Data Records)

Syslog with CSV (Comma-separated Values)

Q102. You have installed the NVIDIA Container Toolkit and are attempting to run a container with GPU support. However, the ‘docker run’ command fails with an error indicating that the NVIDIA runtime is not found. You have already verified that the NVIDIA Container Toolkit is installed, and the Docker daemon has been restarted. What is the most likely cause of this error?

The ‘/etc/docker/daemon.json’ file is missing or has incorrect configuration settings related to the NVIDIA runtime.

The NVIDIA driver version is incompatible with the CUDA version specified in the container image.

The ‘nvidia-container-runtime’ package is not installed.

The container image is corrupted and needs to be rebuilt.

The system doesn’t have a GPU.

Q103. You’re designing a data center network for inference workloads. The primary requirement is high availability. Which of the following considerations are MOST important for your topology design?

Minimizing hop count

Implementing redundant paths

Using the cheapest possible switches

Prioritizing north-south bandwidth over east-west bandwidth

Centralized routing

Q104. You are tasked with diagnosing performance issues on a GPU server running a large-scale HPC simulation. The simulation utilizes multiple GPUs and InfiniBand for inter-GPU communication. You suspect that RDMA (Remote Direct Memory Access) is not functioning correctly. How would you comprehensively test and verify the proper operation of RDMA between the GPUs?

Use ‘ping’ to verify basic network connectivity between the server’s InfiniBand interfaces.

Employ and from the ‘perftest’ suite to measure RDMA bandwidth and latency between GPUs.

Run ‘nvidia-smi topo -m’ to check the GPU interconnect topology and verify that NVLink or PCle is being used for communication.

Utilize NCCL’s internal diagnostic tools to verify proper inter-GPU communication within the simulation.

Monitor CPU utilization during the simulation; high CPU usage suggests that RDMA is not offloading communication effectively.

Q105. You’ve flashed the BlueField OS to your SmartNlC, but you need to customize the kernel command line arguments (bootargs) to enable a specific feature. Where is the MOST appropriate place to modify these arguments for persistent changes that survive reboots?

In the bootloader configuration file (e.g., extlinux.conf or grub.cfg) on the BlueFieId’s flash memory.

In the ‘/etc/default/grub’ file on the BlueField OS, followed by updating the GRUB configuration.

Directly in the kernel image file itself using a hex editor.

Passing it as an argument to bfboot during deployment.

In the ‘/proc/cmdline’ file. This allows immediate changes.

Q106. You are troubleshooting a performance issue with NVMe-oF traffic being accelerated by a BlueField-2 DPU. You suspect a problem with the RDMA configuration. Which of the following ‘perfquery” commands would provide the MOST relevant information to diagnose potential RDMA issues such as packet loss or congestion?

‘perfquery -x’ (general link statistics)

‘perfquery -s;’ (switch statistics)

‘perfquery (QOS statistics)

‘perfquery -P (port counters including packet loss and congestion)

‘perfquery -G’ (global counters)

Q107. You are tasked with automating the BlueField OS deployment process across a large number of SmartNICs. Which of the following methods is MOST suitable for this task?

Manually flashing each SmartNIC using the ‘bfboot utility on a workstation.

Using a network boot (PXE) server to deploy the BlueField OS image over the network. This allows centralized management and scalability.

Creating a custom ISO image with the BlueField OS and booting each SmartNIC from a USB drive.

Utilizing the ‘dd’ command to directly copy the image to each SmartNIC’s flash memory.

Utilizing a custom-built python script to flash each individual card, controlled from a central server. This method supports parallel flashing.

Q108. You are upgrading an AI server with new NVIDIAA800 GPUs and require 400GbE connectivity. After installing the new QSFP-DD transceivers and connecting the fiber cables, the link does not come up. You suspect a polarity issue. Assuming you are using MPO/MTP connectors, which of the following steps would BEST help diagnose and rectify a potential polarity mismatch? (Choose TWO)

Use an Optical Time Domain Reflectometer (OTDR) to verify cable integrity.

Consult the cable manufacturer’s documentation to verify the MPO/MTP key orientation and pinout configuration and ensure it aligns with the transceiver requirements.

Swap the transmit (TX) and receive (RX) fibers at one end of the connection.

Replace the QSFP-DD transceivers with known working units.

Use a fiber optic polarity tester to confirm correct TX/RX mapping through the entire cable assembly.

Q109. You have a server with 8 NVIDIA A100 GPUs. You want to configure each GPU to be used by a different user, ensuring resource isolation and preventing one user’s workload from monopolizing the entire GPU. Which NVIDIA technology is most suitable for this scenario?

NVIDIA CUDA MPS (Multi-Process service)

NVIDIA vGPU (virtual GPU)

NVIDIA SLI (Scalable Link Interface)

NVIDIA NVLink

NVIDIA MIG (Mufti-instance GPU)

Q110. Consider the following ‘Ispci’ output snippet after installing an NVIDIA GPU:
03:00.0 VGA compatible controller: NVIDIA Corporation Device 2236 (rev al) Subsystem: Dell Device 1234 Kernel driver in use: nvidia Kernel modules: nvidiafb, nouveau, nvidia drm, nvidia What does this output indicate, and what is the POTENTIAL issue?

The GPU is correctly installed, and the proprietary NVIDIA driver is loaded.

The GPU is installed, but the open-source ‘nouveau’ driver is loaded instead of the proprietary NVIDIA driver, which could lead to suboptimal performance.

Solution: Blacklist ‘nouveau’ and ensure the NVIDIA driver is correctly configured.

The GPU is not properly installed, as indicated by ‘Device 2236’. Solution: Re-seat the GPU.

The GPU is installed, but the ‘nvidiafb’ driver is causing conflicts. Solution: Remove the ‘nvidiafb’ module.

The output indicates a hardware failure and requires a replacement of the GPU.

Q111. Which of the following is the MOST critical consideration when planning the cooling strategy for a server rack containing multiple NVIDIA A100 GPUs?

Ensuring the server room temperature is kept below 25 degrees Celsius.

Optimizing airflow to ensure hot air is efficiently exhausted from the rack and cool air is drawn in.

Using liquid cooling for the CPUs, but air cooling for the GPUs.

Applying thermal paste to the GPU memory chips.

Increasing the fan speed of the server chassis fans to maximum.

Q112. You are troubleshooting an issue where a Docker container utilizing NVIDIA GPUs intermittently fails with a ‘CUDA ERROR OUT OF MEMORY error. The host system has sufficient memory and the individual GPU has enough memory as well. You suspect that the problem might be related to how memory is being allocated within the container environment. What steps can you take to investigate and potentially mitigate this issue?

Increase the shared memory size for the container using the ‘-shm-size’ flag when running the container.

Set the environment variable inside the container to limit the number of GPUs visible to the application.

Adjust the environment variable inside the container to ensure consistent GPU ordering.

Monitor GPU memory usage both inside and outside the container using ‘nvidia-smi’ to identify memory leaks or excessive allocation.

Lower the compute capability using ‘-compute’ parameter on docker run.

Q113. You are tasked with upgrading the NVIDIA driver on a Kubernetes node hosting GPU-accelerated A1 workloads. To minimize downtime and ensure a smooth transition, which sequence of steps should you follow?

Drain the node, upgrade the driver, reboot the node, and uncordon it.

Upgrade the driver directly on the node, reboot the node, and let Kubernetes automatically reschedule the workloads.

Cordon the node, upgrade the driver, reboot the node, and uncordon it.

Delete all pods running on the node, upgrade the driver, reboot the node, and recreate the pods.

Upgrade the NVIDIA container toolkit, then upgrade the driver, reboot the node, and uncordon it.

Q114. 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.

Q115. Consider an AI server equipped with two NVIDIAAI 00 GPUs interconnected with NVLink. You want to maximize the memory bandwidth available to a CUDA application. You observe that the application’s performance doesn’t scale linearly with the number of GPUs. Which of the following coding techniques or configurations could potentially improve inter-GPU memory access performance?

Ensure all memory allocations are performed on GPU O to minimize data transfer.

Use CUDA-aware MPl for inter-GPU communication to leverage NVLink.

Employ Unified Memory (I-JM) with prefetching to automatically migrate data between GPUs as needed.

Manually manage data transfers between GPUs using ‘cudaMemcpyPeer’ to exploit NVLink bandwidth. Choose the GPU with more free memory for allocations.

Disable NVLink to force the application to use PCle, which might provide more consistent performance.

Q116. When deploying BlueField OS using PXE boot, which of the following files on the PXE server is responsible for specifying the kernel, initrd, and device tree files to be loaded by the client?

dhcpd .conf

pxelinux.cfg/default

tftpboot/pxelinux.0

/boot/grub/grub.cfg

tftpboot/lpxelinux.0

Q117. You are running a large-scale distributed training job on a cluster of AMD EPYC servers, each equipped with multiple NVIDIAA100 GPUs. You are using Slurm for job scheduling. The training process often fails with NCCL errors related to network connectivity. What steps can you take to improve the reliability of the network communication for NCCL in this environment? Choose the MOST appropriate answers.

Ensure that the InfiniBand or RoCE network is properly configured and that all servers can communicate with each other over the network. Verify the network interface names and IP addresses in the NCCL configuration.

Use the Slurm ‘srun’ command with the ‘-mpi=pmi2 option to launch the training job. This ensures that Slurm properly initializes the MPl environment and sets the NCCL environment variables.

Increase the ‘NCCL CONNECT TIMEOUT and *NCCL TIMEOUT environment variables to allow for longer network delays.

Disable the firewall on all servers to allow unrestricted network communication.

Decrease the batch size to reduce the amount of data transferred over the network.

Q118. You’re optimizing an Intel Xeon server with 4 NVIDIAAIOO GPUs for a computer vision application that uses CODA. You notice that the GPU utilization is fluctuating significantly, and performance is inconsistent. Using ‘nvprof, you identify that there are frequent stalls in the CUDA kernels due to thread divergence. What are possible causes and solutions?

The input data is not properly aligned in memory. Ensure that data is aligned to 128-byte boundaries using aligned memory allocation techniques.

The CUDA code contains conditional branches that lead to different execution paths for different threads within the same warp. Rewrite the CUDA code to minimize branching and favor uniform execution paths within warps.

The GPUs are overheating, causing thermal throttling. Improve the server’s cooling.

The CUDA compiler is generating suboptimal code. Try using different compiler optimization flags (e.g., ‘-O3’ or ‘-ftz=true’).

The CUDA driver version is incompatible with the CUDA toolkit version. Update the CUDA driver to a compatible version.

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Authentic NCP-AII Dumps – Free PDF Questions to Pass [Q98-Q118]

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