NVIDIA NCP-AII OFFICIAL CERT GUIDE | NCP-AII NEW QUESTIONS

NVIDIA NCP-AII Official Cert Guide | NCP-AII New Questions

NVIDIA NCP-AII Official Cert Guide | NCP-AII New Questions

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NVIDIA AI Infrastructure Sample Questions (Q264-Q269):

NEW QUESTION # 264
You observe high latency and low bandwidth between two GPUs connected via an NVLink switch. You suspect a problem with the NVLink link itself. Which of the following methods would be the most effective in diagnosing the physical NVLink link health?

  • A. Examining system logs for NVLink-related error messages.
  • B. Running a CUDA-aware memory bandwidth test specifically designed for NVLink.
  • C. Using 'ping' to check network connectivity between the servers.
  • D. Physically inspecting the NVLink cables for damage.
  • E. Using 'iperf3' to measure network throughput between the servers.

Answer: A,B,D

Explanation:
A CUDA-aware memory bandwidth test can specifically measure the NVLink link's performance. System logs can reveal hardware- level errors. Physical inspection can identify damaged cables. 'iperf3' and 'pings are network-level tools and don't directly test the NVLink link. Checking for error messages in System Logs also helps identify potential issues related to the NVLink switch and the link connections.


NEW QUESTION # 265
You are observing high latency in your GPU-accelerated inference service deployed on Kubernetes. You suspect that GPU resource contention might be the cause. What steps can you take to diagnose and mitigate this issue within the Kubernetes environment? (Multiple Answers)

  • A. Utilize MIG (Multi-lnstance GPU) to partition GPUs and isolate workloads.
  • B. Monitor GPU utilization metrics (e.g., GPU utilization, memory usage) using tools like 'nvidia-smi' or Prometheus and Grafana.
  • C. Implement resource quotas to limit the GPU resources that each namespace can consume.
  • D. Restarting the cluster periodically.
  • E. Increase the number of replicas in the deployment to distribute the load across more GPUs.

Answer: A,B,C

Explanation:
Monitoring GPU utilization helps identify resource contention. Resource quotas prevent one namespace from consuming excessive GPU resources. MIG allows partitioning GPUs for workload isolation. Increasing replicas (option D) might help, but it doesn't address the underlying contention issue. Restarting the cluster is not a solution for ongoing contention.


NEW QUESTION # 266
You are tasked with selecting transceivers for a new NVIDIA Quantum-2 InfiniBand switch deployment. The primary requirement is to minimize power consumption while maintaining 400Gbps bandwidth over short distances (up to 50 meters). Which transceiver type would offer the BEST power efficiency in this scenario?

  • A. QSFP-DD DR4
  • B. QSFP-DD SR4
  • C. QSFP-DD AOC
  • D. QSFP-DD LR8
  • E. QSFP-DD SR8

Answer: B

Explanation:
SR4 transceivers are known for their relatively low power consumption compared to other 400GbE transceiver types. This is because they use a simpler modulation scheme and shorter reach, requiring less power for signal amplification and processing. LR8 and DR4 are designed for longer distances and consume more power. AOCs, while convenient, are not typically the most power-efficient option. SR8 may consume slightly higher power than SR4 but provides better performance in certain scenarios.


NEW QUESTION # 267
Consider this Python code snippet used for loading data for a deep learning model:

Assuming the images are stored on a remote file system accessed over NFS, and training is slow due to I/O bottlenecks, which change to the 'DataLoader' would MOST directly improve performance?

  • A. Increase 'batch_size' significantly (e.g., to 256).
  • B. Decrease 'num workers' to 1.
  • C. Set in the DataLoader.
  • D. Increase 'num_workers' to a value equal to the number of CPU cores on each training node.
  • E. Implement multithreaded data loading inside _getitem

Answer: D

Explanation:
Explanation: Increasing 'num_workerS allows the DataLoader to load data in parallel, which can help to overcome the I/O bottleneck when using NFS. Setting it to the number of CPU cores on each training node often yields the best performance. Increasing 'batch_size' helps, but it has diminishing returns if I/O is the primary bottleneck. Decreasing 'num_workers' will worsen the bottleneck. Primarily improves GPU memory transfer performance, not I/O performance from NFS. Multithreading inside _getitem is more complex and can lead to contention if not implemented carefully.


NEW QUESTION # 268
You're setting up a BlueField-3 DPIJ to offload storage virtualization tasks. Specifically, you want to use SPDK (Storage Performance Development Kit) on the DPIJ. What are the MINIMUM required steps to enable SPDK on the BlueField-3 after the DPIJ has been flashed with the appropriate OS image? (Select TWO)

  • A. Configure the Huge Pages settings in the DPU's kernel to allocate sufficient memory for SPDK.
  • B. Enable the SPDK service using 'systemctl enable spdK and 'systemctl start spdk'.
  • C. Install the SPDK packages using the DPU's package manager (e.g., 'apt install spdk').
  • D. Configure the network interfaces on the DPIJ to support RDMA or NVMe-oF, depending on the desired storage protocol.
  • E. Download and compile the SPDK source code directly on the DPU.

Answer: A,C

Explanation:
The minimum steps involve installing the SPDK packages using the DPU's package manager, assuming prebuilt packages are available, and configuring Huge Pages. SPDK relies heavily on Huge Pages for memory management. While configuring network interfaces and starting services are important, installing SPDK and configuring Huge Pages are required first steps. Downloading and compiling from source might be necessary in some cases but not minimally required if packages are available.


NEW QUESTION # 269
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