FREE PDF 2025 NVIDIA NCP-AII: NVIDIA AI INFRASTRUCTURE PASS-SURE EXAM DUMPS DEMO

Free PDF 2025 NVIDIA NCP-AII: NVIDIA AI Infrastructure Pass-Sure Exam Dumps Demo

Free PDF 2025 NVIDIA NCP-AII: NVIDIA AI Infrastructure Pass-Sure Exam Dumps Demo

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Tags: Exam Dumps NCP-AII Demo, NCP-AII Test Guide Online, NCP-AII Free Dumps, NCP-AII Valid Dump, NCP-AII Test Engine Version

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NVIDIA AI Infrastructure Sample Questions (Q111-Q116):

NEW QUESTION # 111
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?

  • A. LMC: 0 indicates that Link Aggregation (LAG) is not enabled on this port, meaning only a single link is being used for communication.
  • B. LMC : 0 is the default and expected value; it has no impact on performance.
  • C. LMC: 0 indicates that the link is down and not functioning correctly.
  • D. LMC : 0 indicates the port is operating at the lowest possible speed.
  • E. LMC : 0 indicates that the Subnet Manager is not running correctly.

Answer: A

Explanation:
LMC (Link Mask Capability) indicates the level of link aggregation enabled. LMC: 0 means that link aggregation is disabled. This means that only one link is being used for communication, which could limit the overall bandwidth and potentially create a bottleneck if multiple GPUs are trying to communicate through that single link. Higher LMC values indicate that multiple links are aggregated together to increase bandwidth.


NEW QUESTION # 112
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 Kubernetes Resource Quota controller.
  • C. The NVIDIA Device Plugin for Kubernetes.
  • D. The NVIDIA Fabric Manager
  • E. The NVIDIA GPU Operator.

Answer: C,E

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 # 113
Consider the following scenario: You have a BlueField-2 DPU installed in a server. You are trying to establish RDMA (Remote Direct Memory Access) communication between the DPU and another server. However, the RDMA connection fails. Which of the following is the most crucial factor to verify in this scenario?

  • A. That the power supply to the DPU is providing sufficient wattage.
  • B. That the correct MTU (Maximum Transmission Unit) is configured on the network interfaces involved in the RDMA connection.
  • C. That the appropriate RDMA kernel modules (e.g., , 'ib_uverbs) are loaded on both the DPUand the remote server.
  • D. That the clocks on both servers are synchronized using NTP (Network Time Protocol).
  • E. That the TCP window size is properly tuned for high-bandwidth communication.

Answer: C

Explanation:
For RDMA to function correctly, the necessary kernel modules must be loaded on both ends of the connection. These modules provide the core functionality for RDMA communication. While MTU and other network settings are important, the absence of the RDMA modules will prevent the connection from being established in the first place. Clock synchronization and TCP window size are less directly related to the initial RDMA connection failure. Power supply issues would likely manifest in other ways.


NEW QUESTION # 114
You're optimizing an Intel Xeon server with 4 NVIDIA GPUs for inference serving using Triton Inference Server. You've deployed multiple models concurrently. You observe that the overall throughput is lower than expected, and the GPU utilization is not consistently high.
What are potential bottlenecks and optimization strategies? (Select all that apply)

  • A. Model loading and unloading overhead. Use model ensemble or dynamic batching to reduce frequency.
  • B. Insufficient PCle bandwidth between CPU and GPIJs. Reconfigure PCle lanes to improve bandwidth allocation to each GPIJ.
  • C. The GPUs are underutilized due to small batch sizes. Implement dynamic batching to increase batch sizes.
  • D. The models are memory-bound. Reduce the model precision (e.g., FP32 to FP16 or INT8).
  • E. Insufficient CPU cores to handle the model loading and preprocessing requests. Increase the number of Triton instance groups for CPU-based models.

Answer: A,C,D,E

Explanation:
Multiple factors can contribute to low throughput in inference serving. Model loading overhead is significant, and dynamic batching is crucial to maximize throughput. Insufficient CPU cores and memory constraints on the GPU also limit performance. Model precision reduction helps reduce memory footprint and increase throughput. While PCle bandwidth is a factor, it is often not the primary bottleneck in inference serving.


NEW QUESTION # 115
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?

  • A. Adjust the application code to use less GPIJ memory dynamically.
  • B. Utilize CUDA MPS to dynamically allocate GPU resources to different processes.
  • C. Implement a script that monitors GPU utilization and automatically adjusts Kubernetes resource quotas to match.
  • D. Leverage a GPU virtualization platform with dynamic resource allocation capabilities that integrates with MIG.
  • E. Use 'nvidia-smi' to manually destroy and recreate MIG instances with different sizes as needed.

Answer: D

Explanation:
Explanation: Dynamically resizing MIG instances requires a mechanism that can automatically adjust the underlying GPU partitioning based on workload demands. The most appropriate method is leveraging a GPU virtualization platform (C) that offers dynamic resource allocation and integrates with MIG. These platforms can monitor resource utilization and automatically resize MIG instances accordingly. Manually resizing (A) is impractical for dynamic adjustments. Kubernetes resource quotas (B) control container resource limits, not the underlying MIG configuration. CUDA MPS (D) allows sharing a single GPU but doesn't resize MIG instances. Adjusting application code (E) doesn't address the need for dynamic MIG resizing.


NEW QUESTION # 116
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