2025 NCP-AII: PERFECT RELIABLE NVIDIA AI INFRASTRUCTURE DUMPS QUESTIONS

2025 NCP-AII: Perfect Reliable NVIDIA AI Infrastructure Dumps Questions

2025 NCP-AII: Perfect Reliable NVIDIA AI Infrastructure Dumps Questions

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NVIDIA AI Infrastructure Sample Questions (Q290-Q295):

NEW QUESTION # 290
You are deploying a multi-node A1 training cluster using Kubernetes, with each node equipped with multiple NVIDIA GPUs. You want to ensure that the Kubernetes scheduler is aware of the GPU resources available on each node and can efficiently allocate GPU-enabled pods to the appropriate nodes. Besides installing the NVIDIA Container Toolkit, what other components are essential for enabling GPU-aware scheduling in Kubernetes?

  • A. The Kubernetes Horizontal Pod Autoscaler (HPA).
  • B. The NVIDIA Device Plugin for Kubernetes.
  • C. The NVIDIA GPU Operator.
  • D. The Kubernetes Resource Quota controller.
  • E. The NVIDIA Fabric Manager

Answer: B,C

Explanation:
The NVIDIA Device Plugin for Kubernetes (A) is essential for advertising the GPU resources to the Kubernetes scheduler. It allows Kubernetes to understand that GPUs are available and track their usage. The NVIDIA GPU Operator (C) simplifies the deployment and management of NVIDIA drivers and other components required for GPU support in Kubernetes, including the device plugin. The Resource Quota controller (B) is useful for limiting resource consumption but doesn't directly enable GPU-aware scheduling. HPA (D) is used for autoscaling based on CPU or memory utilization, not GPU utilization. Fabric Manager is for managing GPU interconnect and not related.


NEW QUESTION # 291
You are tasked with configuring a BlueField-2 DPIJ to offload network virtualization functions. Which Mellanox OFED command is MOST crucial for verifying that SR-IOV is correctly enabled and functional on the host and the DPU, prior to configuring vPorts?

  • A. 'ethtool -i to check driver information.
  • B. 'Ispci -vv' to check for the presence of Virtual Functions (VFs) and their assignment.
  • C. 'mlxlink' to check link speed and FEC settings.
  • D. 'ifconfig' or 'ip addr show' to verify IP address assignments to network interfaces.
  • E. 'mst statuS to verify the status of Mellanox devices and their firmware.

Answer: B

Explanation:
"Ispci -vv' is the most crucial command. It shows the presence of Virtual Functions (VFs) which confirms SR-IOV is enabled. It also shows the device IDs assigned, allowing verification that the host and DPIJ are correctly seeing and assigning VFs to the network interfaces. While other commands are useful for general network diagnostics, they don't directly verify SR-IOV functionality like 'Ispci -vv' does.


NEW QUESTION # 292
After configuring MIG on an NVIDIAAIOO GPU, you run 'nvidia-smu and observe that all MIG instances are in the 'Disabled' state.
Which of the following are potential reasons for this issue? (Select all that apply)

  • A. The necessary NVIDIA drivers are not installed or are incompatible with the GPU.
  • B. The MIG instances are correctly configured but have not been allocated to any processes.
  • C. The GPU is not in MIG mode.
  • D. The 'nvidia-persistenced' service is not running.
  • E. The system's power supply is insufficient.

Answer: A,C,D

Explanation:
MIG instances being in a 'Disabled' state indicates a fundamental problem with the MIG configuration. Incompatible drivers (A) will prevent the instances from being properly initialized. If the GPU is not explicitly placed into MIG mode (B), no MIG instances will be available. The 'nvidia-persistenced' service (D) ensures that driver state persists across reboots, and its absence can cause MIG instances to revert to a disabled state. While unallocated instances (C) will exist, they should be in an 'Idle' state, not 'Disabled'. An insufficient power supply (E) might prevent the GPU from functioning correctly, but it's less likely to specifically cause a 'Disabled' MIG state.


NEW QUESTION # 293
A large language model (LLM) training job is running across multiple NVIDIAAI 00 GPUs in a cluster. You observe that the GPUs within a single server are communicating efficiently via NVLink, but the inter-server communication over Ethernet is becoming a bottleneck. Which of the following strategies, focusing on cable and transceiver selection, would MOST effectively address this inter-server communication bottleneck? (Choose TWO)

  • A. Upgrade inter-server connections to the highest available Ethernet speed (e.g., from 100GbE to 400GbE) using appropriate transceivers and fiber optic cables.
  • B. Implement InfiniBand as the interconnect technology for inter-server communication, utilizing appropriate InfiniBand cables and transceivers.
  • C. Replace all existing transceivers with Active Optical Cables (AOCs).
  • D. Replace existing Cat6 Ethernet cables with Cat8 cables.
  • E. Reduce the batch size of the LLM training job.

Answer: A,B

Explanation:
Upgrading to higher Ethernet speeds (e.g., 400GbE) with appropriate transceivers and fiber significantly increases bandwidth for inter-server communication. Implementing InfiniBand, a technology designed for high-performance computing interconnects, offers even better performance than Ethernet. Cat8 cables improve Ethernet signal quality but don't fundamentally increase bandwidth. Reducing batch size reduces the amount of data transferred but might impact training convergence. While AOCs can improve signal quality, they don't address the underlying bandwidth limitations as effectively as faster Ethernet or InfiniBand.


NEW QUESTION # 294
You have a GPU-intensive application that requires the latest features of CUDA 12. However, your host system's NVIDIA driver is only compatible with CUDA 11.8. What steps can you take to enable your application to use CUDA 12 within a Docker container, without upgrading the host driver?

  • A. Mount the CUDA 12 libraries from a separate Docker volume into the container and configure the accordingly.
  • B. Upgrade the NVIDIA driver on the host system to the latest version compatible with CUDA 12.
  • C. Downgrade the application to use CUDA 11.8 to match the host's driver version.
  • D. Use a Docker image based on 'nvidia/cuda:12.0-base-ubuntu20.04'. The NVIDIA Container Toolkit will automatically handle the driver compatibility between the host and the container.
  • E. Install CUDA 12 inside the Docker container and set the 'CUDA DRIVER VERSION' environment variable to match the host driver version.

Answer: D

Explanation:
The NVIDIA Container Toolkit enables compatibility between the host driver and the CUDA version within the container (B). Using a Docker image based on will allow your application to leverage CUDA 12 features. Upgrading the host driver (A) is an option but not necessary and may introduce other compatibility issues. Setting (C) is not a standard or reliable approach. Mounting CUDA libraries from a volume (D) is complex and might not resolve driver version mismatches. Downgrading the application (E) avoids the problem but sacrifices access to CUDA 12 features. Because NVIDIA ensures a degree of backwards compatibility, the newer toolkit in the container can often work with an older driver on the host.


NEW QUESTION # 295
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