NCP-AII試験無料問題集（301題）「NVIDIA AI Infrastructure 認定」

出題：1

You're deploying a distributed training workload across multiple NVIDIAAIOO GPUs connected with NVLink and InfiniBand. What steps are necessary to validate the end-to-end network performance between the GPUs before running the actual training job? (Select all that apply)

A. Manually inspect the physical cabling of NVLink bridges and InfiniBand connections.

B. Run NCCL tests (e.g., to measure NVLink bandwidth and latency between GPUs on the same node.

C. Employ 'iperf3' or 'nc' to measure TCP/UDP bandwidth between nodes over the InfiniBand network.

D. Use 'ibstat' to verify the status and link speed of the InfiniBand interfaces on each node.

E. Ping all nodes to confirm basic network connectivity

正解：B,C,D 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：2

You are setting up a virtualized environment (using VMware vSphere) to run GPU-accelerated workloads. You have multiple physical GPUs in your server and want to assign specific GPUs to different virtual machines (VMs) for dedicated access. Which vSphere technology would BEST support this?

A. VMware DRS (Distributed Resource Scheduler)

B. VMware vMotion

C. VMware DirectPath I/O (Passthrough)

D. VMware vGPU

E. VMware High Availability (HA)

正解：C 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：3

You are installing four NVIDIAAIOO GPUs in a server, and after installation, you observe that the PCle link speed for one of the GPUs is running at x8 instead of the expected x16. What could be the POSSIBLE causes for this reduced PCle link speed?

A. The BIOS/UEFI is configured to limit the PCle link speed for that slot.

B. All of the above

C. The GPU is faulty.

D. The CPU does not have enough PCle lanes to support all GPUs at x16.

E. The PCle slot is only wired for x8 speed.

正解：B 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：4

You're deploying a large language model for inference using NVIDIA Triton Inference Server. You need to validate that the server can handle the expected query load while maintaining acceptable latency. Which tools and metrics are most relevant for this validation?

A. Check CPU utilization using 'top'

B. Employ a load testing tool (e.g., 'locust' , 'JMeter') to simulate client requests and measure response times.

C. Utilize Triton's built-in metrics endpoint to track latency, throughput, and request queue length.

D. All of the above

E. Use 'nvidia-smi' to monitor GPIJ utilization and memory usage.

正解：D 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：5

An AI server with 8 GPUs is experiencing random system crashes under heavy load. The system logs indicate potential memory errors, but standard memory tests (memtest86+) pass without any failures. The GPUs are passively cooled. What are the THREE most likely root causes of these crashes?

A. Network congestion causing intermittent data corruption during distributed training.

B. GPIJ memory errors that are not detectable by standard CPU-based memory tests.

C. Insufficient airflow within the server, leading to overheating of the GPUs and VRMs.

D. A faulty power supply unit (PSU) that is unable to provide stable power under peak load.

E. Incompatible NVIDIA driver version with the installed Linux kernel.

正解：B,C,D 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：6

You're monitoring the storage I/O for an AI training workload and observe high disk utilization but relatively low CPU utilization. Which of the following actions is LEAST likely to improve the performance of the training job?

A. Implementing data prefetching to load data into memory before it's needed.

B. Switching from HDDs to NVMe SSDs.

C. Increasing the batch size of the training job.

D. Adding more RAM to the system.

E. Reducing the number of parallel data loading threads.

正解：E 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：7

You are deploying a BlueField-2 DPU-based server in a VMware vSphere environment. Which network virtualization technology is most commonly used in conjunction with the DPU to provide accelerated networking and security features within the virtualized environment?

A. VXLAN (Virtual Extensible LAN)

B. LACP (Link Aggregation Control Protocol)

C. SR-IOV (Single Root 1/0 Virtualization)

D. IPsec (Internet Protocol Security)

E. GRE (Generic Routing Encapsulation)

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：8

After installing NGC CLI using pip, you encounter 'ngc' command not found error even though pip install reported successful. What can be the cause?

A. The shell needs to be reloaded or a new terminal session initiated for PATH changes to take effect.

B. NGC CLI only works inside Docker containers.

C. The host's operating system is not supported by NGC CLI.

D. The NGC CLI installation was corrupted. Run 'pip install -force-reinstall nvidia-cli'

E. The python executable where NGC CLI got installed is not in the system PATH.

正解：A,E 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：9

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?

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

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

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

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

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

正解：C 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：10

You are tasked with configuring an NVIDIA NVLink Switch system. After physically connecting the GPUs and the switch, what is the typical first step in the software configuration process?

A. Installing the latest NVIDIA drivers on all connected GPUs.

B. Updating the firmware of the NVLink Switch.

C. Installing the NVLink Switch management software.

D. Configuring the system BIOS to enable NVLink support.

E. Running a memory bandwidth test between all connected GPUs.

正解：B 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：11

You are troubleshooting a network performance issue in your NVIDIA Spectrum-X based A1 cluster. You suspect that the Equal-Cost Multi-Path (ECMP) hashing algorithm is not distributing traffic evenly across available paths, leading to congestion on some links. Which of the following methods would be MOST effective for verifying and addressing this issue?

A. Use switch telemetry tools (e.g., NVIDIA What's Up Gold, Mellanox NEO, or similar) to monitor link utilization across all available paths between the nodes. Look for significant imbalances in traffic volume.

B. Use 'ping' or 'traceroute' to analyze the paths taken by packets between the affected nodes. If they always take the same path, ECMP is likely not working correctly.

C. Restart the switches to force the ECMP hashing algorithm to recalculate paths.

D. Disable ECMP entirely and rely solely on static routing.

E. Reduce the TCP window size.

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：12

You are managing a cluster of GPU servers for deep learning. You observe that one server consistently exhibits high GPU temperature during training, causing thermal throttling and reduced performance. You've already ensured adequate airflow. Which of the following actions would be MOST effective in addressing this issue?

A. Increase the fan speed of the GPU cooler using 'nvidia-smi --fan'.

B. Reduce the ambient temperature of the data center.

C. Update the NVIDIA drivers to the latest version.

D. Re-seat the GPU in its PCle slot to ensure proper contact and heat dissipation.

E. Lower the GPU power limit using 'nvidia-smi -power-limit*.

正解：A,D 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：13

Your deep learning training job that utilizes NCCL (NVIDIA Collective Communications Library) for multi-GPU communication is failing with "NCCL internal error, unhandled system error" after a recent CUDA update. The error occurs during the 'all reduce' operation.
What is the most likely root cause and how would you address it?

A. GPU Direct RDMA is not properly configured. Check 'dmesg' for errors and ensure RDMA is enabled.

B. Insufficient shared memory allocated to the CUDA context. Increase the shared memory limit using 'cudaDeviceSetLimit(cudaLimitSharedMemory, new_limity.

C. Incompatible NCCL version with the new CUDA version. Update NCCL to a version compatible with the installed CUDA version.

D. Firewall rules blocking inter-GPU communication. Configure the firewall to allow communication on the NCCL-defined ports (typically 8000-8010).

E. Faulty network cables used for inter-node communication (if the training job spans multiple servers). Replace the network cables with certified high-speed cables.

正解：C 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：14

You are tasked with installing the NGC CLI on a host that does not have direct internet access. You have downloaded the NGC CLI package to a local repository. Which of the following steps are required to successfully install and configure the NGC CLI in this offline environment?

A. Configure the NGC CLI to point to your local package repository by setting the environment variable.

B. Transfer the NGC CLI package to the host and install it using 'pip install .whl'.

C. Only copying the whl file is sufficient, NGC CLI dependencies are always local

D. Manually download and install all dependencies of the NGC CLI package using 'pip install --no-index --find-links=/path/to/dependencies .whl'.

E. Run 'ngc config set' to configure the API key, pointing to a local configuration file.

正解：A,B,D,E 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：15

A security policy requires you to log all NGC CLI commands executed on a specific host. How can you achieve this without modifying the NGC CLI source code?

A. Implement system-level auditing using tools like 'auditd' (Linux) or Windows Event Logging to capture all executed commands, including NGC CLI commands.

B. Configure the NGC CLI to send all commands to a remote syslog server.

C. Use the 'history' command in Linux to view the command history for the user account used to execute the NGC CLI commands.

D. NGC CLI automatically logs all commands to a file in the '/var/log/ngc' directory.

E. Create a wrapper script around the 'ngc' command that logs the command and its arguments before executing the actual NGC CLI command.

正解：A,E 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：16

You need to verify the integrity of the BlueField OS image before flashing it to the SmartNIC. Which method would provide the strongest guarantee that the image has not been tampered with?

A. Verifying the SHA256 checksum of the image against the published SHA256 checksum.

B. Checking the MD5 checksum of the image against the published MD5 checksum.

C. Downloading the image from a trusted source.

D. Comparing the image size with the expected size from the download source.

E. Utilizing gpg to verify the image.

正解：A,E 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：17

Which of the following techniques are effective for improving inter-GPU communication performance in a multi-GPU Intel Xeon server used for distributed deep learning training with NCCL?

A. Configuring NCCL to use the correct network interface and transport protocol (e.g., 1B, Socket).

B. Disabling CPU frequency scaling to maintain consistent performance.

C. Enabling PCle peer-to-peer transfers between GPUs.

D. Utilizing InfiniBand or RoCE interconnects if available.

E. Increasing the system RAM size to minimize data transfer to disk.

正解：A,C,D 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：18

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?

A. Resource Quotas

B. Node Affinity

C. Taints and Tolerations

D. Device Plugins API

E. Topology Manager

正解：E 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：19

Consider a scenario where you are running a CUDA application on an NVIDIA GPU. The application compiles successfully but crashes during runtime with a *CUDA ERROR ILLEGAL ADDRESS* error. You've carefully reviewed your code and can't find any obvious out- of-bounds memory accesses. What advanced debugging techniques could help you pinpoint the source of this error?

A. Reduce the block size used in CUDA kernels to decrease the likelihood of shared memory conflicts.

B. Enable ECC (Error Correction Code) memory on the GPU to detect and correct memory errors.

C. Employ the CUDA Debugger (cuda-gdb) to step through the code and inspect variable values and memory contents.

D. Utilize NVIDIA Nsight Systems to profile the application and identify memory allocation patterns.

E. Use 'cuda-memcheck' to detect memory access errors at runtime.

正解：C,D,E 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

NCP-AII試験無料問題集「NVIDIA AI Infrastructure 認定」