HERE IS THE EFFORTLESS METHOD TO PASS THE NVIDIA NCP-AII EXAM

Here is the Effortless Method to Pass the NVIDIA NCP-AII Exam

Here is the Effortless Method to Pass the NVIDIA NCP-AII Exam

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NVIDIA AI Infrastructure Sample Questions (Q241-Q246):

NEW QUESTION # 241
You are managing an A1 infrastructure based on NVIDIA Spectrum-X switches. A new application requires strict Quality of Service (QOS) guarantees for its traffic. Specifically, you need to ensure that this application's traffic receives preferential treatment and minimal latency.
What combination of Spectrum-X features and configurations would be MOST effective in achieving this?

  • A. Enable broadcast storm protection.
  • B. Disable Adaptive Routing (AR) to ensure that traffic always takes the shortest path.
  • C. Use VLAN tagging to isolate the application's traffic into a separate virtual network.
  • D. Increase the MTIJ size on all interfaces to reduce packet fragmentation and overall latency.
  • E. Configure DiffServ Code Point (DSCP) marking on the application's traffic, map these DSCP values to specific traffic classes within the Spectrum-X switch, and configure Weighted Fair Queueing (WFQ) or Strict Priority Queueing on the egress ports.

Answer: E

Explanation:
DSCP marking, traffic class mapping, and WFQ/Strict Priority Queueing are fundamental QOS mechanisms. DSCP marking allows you to classify traffic based on application requirements. Traffic classes within the switch provide different levels of service. WFQ and Strict Priority Queueing ensure that high-priority traffic receives preferential treatment on egress ports. The other options are less relevant to QOS guarantees for a specific application.


NEW QUESTION # 242
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. Reduce the TCP window size.
  • E. Disable ECMP entirely and rely solely on static routing.

Answer: A

Explanation:
Switch telemetry tools provide the most direct and comprehensive way to monitor link utilization and identify imbalances in traffic distribution caused by ECMP. While 'ping' and 'traceroute' can provide path information, they don't give insight into traffic volume. Restarting the switches might temporarily alleviate the issue but doesn't address the underlying problem with the ECMP hashing. Disabling ECMP is a last resort and can reduce overall bandwidth.


NEW QUESTION # 243
What is the role of GPUDirect RDMA in an NVLink Switch-based system, and how does it improve performance?

  • A. It enables direct communication between GPUs and storage devices, bypassing the network interface.
  • B. It facilitates the virtualization of GPUs, allowing multiple virtual machines to share a single physical GPIJ.
  • C. It encrypts data transmitted between GPUs, enhancing security.
  • D. It provides a mechanism for GPUs to offload compute-intensive tasks to the CPU, improving overall system throughput.
  • E. It allows GPUs to directly access each other's memory without involving the CPIJ, reducing latency and CPU overhead.

Answer: E

Explanation:
GPUDirect RDMA enables direct memory access between GPUs, bypassing the CPU and reducing latency. This significantly improves performance for applications that require frequent data transfers between GPUs. Other options describe functionalities that are not associated with RDMA in this context.


NEW QUESTION # 244
You are designing an AI infrastructure cluster for training large language models (LLMs). The dataset consists of 10TB of image data and 5TB of text dat a. You estimate that intermediate training data (checkpoints, temporary files) will require an additional 20TB of storage. You want to use a parallel file system for optimal performance. Considering a replication factor of 2 for data redundancy and a 20% overhead for file system metadata, what is the minimum raw storage capacity you should provision?

  • A. 100.8 TB
  • B. 42 TB
  • C. 70 TB
  • D. 84 TB
  • E. 92.4 TB

Answer: E

Explanation:
Total data size: IOTB + 5TB + 20TB = 35TB. With a replication factor of 2, the storage required is 35TB 2 = 70TB. Adding 20% overhead for metadata, we get 70TB 1.2 = 84 T B. Therefore, the minimum raw storage capacity is 84 + 8.4 = 92.4 TB. Overhead needs to be calcualted from after replication is implemented, so replication + 20% overhead.


NEW QUESTION # 245
Your A1 inference server utilizes Triton Inference Server and experiences intermittent latency spikes. Profiling reveals that the GPU is frequently stalling due to memory allocation issues. Which strategy or tool would be least effective in mitigating these memory allocation stalls?

  • A. Increasing the GPU's TCC (Tesla Compute Cluster) mode priority.
  • B. Enabling CUDA graph capture to reduce kernel launch overhead.
  • C. Optimize the model using TensorRT.
  • D. Reducing the model's memory footprint by using quantization or pruning techniques.
  • E. Using CIJDA memory pools to pre-allocate memory and reduce allocation overhead during inference requests.

Answer: A

Explanation:
CUDA memory pools directly address memory allocation overhead. CUDA graph capture reduces kernel launch overhead, which can indirectly reduce memory pressure. Model quantization/pruning reduces the overall memory footprint. Optimizing using TensorRT reduces memory footprint. Increasing TCC priority primarily affects preemption behavior and doesn't directly address memory allocation issues. Therefore it will have less impact than others.


NEW QUESTION # 246
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