HOW YOU CAN ACE YOUR EXAM PREPARATION WITH GETVALIDTEST NCP-AII EXAM QUESTIONS?

How You Can Ace Your Exam Preparation With GetValidTest NCP-AII Exam Questions?

How You Can Ace Your Exam Preparation With GetValidTest NCP-AII Exam Questions?

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NVIDIA AI Infrastructure Sample Questions (Q242-Q247):

NEW QUESTION # 242
You are managing a Kubernetes cluster with NVIDIA GPUs and want to automatically scale your A1 inference deployments based on GPU utilization. Which of the following tools and configurations would you use to implement Horizontal Pod Autoscaling (HPA) based on GPU metrics?

  • A. Configuring the HPA based on memory utilization.
  • B. Manually adjusting the number of replicas in the deployment based on observed GPIJ utilization.
  • C. Using the Kubernetes Resource Metrics API to directly access GPU utilization metrics and configuring the HPA accordingly.
  • D. Using the standard Kubernetes HPA with CPU utilization as the scaling metric, assuming GPU utilization is correlated with CPU usage.
  • E. Using the NVIDIA Data Center GPU Manager (DCGM) exporter to expose GPU metrics to Prometheus, and configuring the HPA to scale based on these metrics.

Answer: E

Explanation:
The NVIDIA DCGM exporter provides a way to expose GPU metrics to Prometheus, which can then be used by the Kubernetes HPA to automatically scale deployments based on actual GPU utilization. The standard Kubernetes HPA does not natively support GPU metrics. Other options are either inaccurate or manual.


NEW QUESTION # 243
You are using a custom container runtime other than Docker (e.g., containerd) and need to integrate it with the NVIDIA Container Toolkit.
What command would you use to configure the NVIDIA Container Toolkit for this runtime? (Assume your runtime configuration file is located at '/etc/containerd/config.toml')

  • A. 'nvidia-docker runtime configure -runtime=containerd'
  • B. nvidia-ctk runtime install -runtime-containerd'
  • C. nvidia-ctk runtime configure -runtime=custom -config=/etc/containerd/config.tomr
  • D. nvidia-ctk runtime configure
  • E. nvidia-ctk runtime config -runtime=containerd -set-default'

Answer: C

Explanation:
The Anvidia-ctk runtime configure' command is used to configure the NVIDIA Container Toolkit for different container runtimes. When the runtime is not a standard one, the '-runtime=custom' option must be used, and you also provide the path to the configuration file using config=/etc/containerd/config.tomr. There is no 'install' or 'config' subcommand under 'nvidia-ctk runtime' for runtime selection.


NEW QUESTION # 244
A critical AI model training job consistently fails on a specific GPU server in your cluster after running for approximately 24 hours.
Monitoring data shows a sudden drop in GPU power consumption followed by a system reboot. All other GPUs on the server appear normal. The server has redundant PSUs. What is the MOST likely cause?

  • A. Thermal runaway on the GPU due to a failing thermal interface material (TIM) between the GPU die and the heatsink.
  • B. A driver crash, causing the GPU to reset and the system to reboot.
  • C. A transient power supply issue affecting only one of the redundant PSUs, triggering a system-wide protection mechanism.
  • D. ECC memory errors accumulating over time, eventually leading to a non-recoverable system fault.
  • E. A software bug in the A1 model causing a kernel panic specifically triggered after 24 hours of execution.

Answer: A

Explanation:
Thermal runaway (B) is the most probable cause. The 24-hour delay suggests a gradual heat buildup. A failing TIM would cause the GPU to overheat until it triggers a thermal shutdown, resulting in the power drop and reboot. While a PSU issue (C) is possible, redundant PSUs should prevent a complete failure unless one PSU is completely dead and the second PSU is overloaded by the entire load for a short period. The other options are less likely to cause this specific failure pattern.


NEW QUESTION # 245
You are designing an AI infrastructure using NVIDIA HGX AIOO servers. These servers support both PCle Gen4 and NVLink for GPU interconnects. Which statement is MOST accurate concerning the choice between PCle Gen4 and NVLink for inter-GPU communication within a single HGX AIOO server for deep learning training?

  • A. PCle Gen4 offers higher bandwidth and lower latency compared to NVLink, making it the preferred choice for all deep learning workloads.
  • B. PCle Gen4 is more cost-effective and power-efficient than NVLink, making it the optimal choice for smaller AI training datasets.
  • C. The choice between PCle Gen4 and NVLink depends solely on the type of deep learning framework being used; TensorFlow requires PCle Gen4, while PyTorch benefits from NVLink.
  • D. NVLink provides a direct, high-bandwidth, low-latency connection between GPUs, which is generally superior to PCle Gen4 for deep learning training involving frequent inter-GPU communication.
  • E. NVLink and PCle Gen4 offer identical performance for inter-GPU communication; the choice is arbitrary.

Answer: D

Explanation:
NVLink is specifically designed for high-bandwidth, low-latency communication between GPUs, making it superior to PCle Gen4 for deep learning training where GPUs frequently exchange data. NVLink allows GPIJs to share memory directly.


NEW QUESTION # 246
You are tasked with installing a BlueField-2 DPU on a server. After physical installation, the DPU is not recognized by the host OS (Linux). You've verified the power and connection. What is the most likely first step you should take to troubleshoot the issue?

  • A. Replace the BlueField-2 DPU, assuming it's faulty hardware.
  • B. Check the system BIOS settings to ensure that IOMMU (Input/Output Memory Management Unit) is enabled and properly configured.
  • C. Immediately reflash the BlueField-2 DPU firmware with the latest version.
  • D. Check the UEFI settings to ensure that the PCle slot where the DPU is installed is enabled and configured correctly.
  • E. Install the latest NVIDIA drivers on the host OS, specifically the BlueField-related drivers.

Answer: B

Explanation:
IOMMU is crucial for the host OS to properly recognize and manage the DPUs resources. If IOMMIJ is disabled or misconfigured, the DPU might not be detected. The other options are less likely to be the immediate cause of the issue. While drivers are important, IOMMIJ is a fundamental requirement. IJEFI config is also imporant but less likely.


NEW QUESTION # 247
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