NCP-AII VCE DUMPS, EXAMCOLLECTION NCP-AII QUESTIONS ANSWERS

NCP-AII VCE Dumps, Examcollection NCP-AII Questions Answers

NCP-AII VCE Dumps, Examcollection NCP-AII Questions Answers

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NVIDIA AI Infrastructure Sample Questions (Q118-Q123):

NEW QUESTION # 118
You have installed an NVIDIA ConnectX-7 network adapter in an A1 server and configured RDMA over Converged Ethernet (RoCE). During validation, you observe very high latency between two servers communicating over RoCE. Which of the following are potential causes? (Choose two)

  • A. The network switch does not support RoCE.
  • B. Insufficient memory on the network adapter.
  • C. The GPU driver is outdated.
  • D. The network cables are damaged.
  • E. Incorrect MTU size configuration on the network interfaces.

Answer: A,E

Explanation:
RoCE requires specific switch support and a properly configured MTU. Damaged cables could cause packet loss, but usually not consistently high latency. GPU drivers are irrelevant. Network adapter memory is unlikely to cause high latency unless extremely undersized, a less likely scenario than incorrect configuration or lack of RoCE support.


NEW QUESTION # 119
You are deploying a BlueField-3 DPU within a secure environment. You are required to enable secure boot to prevent unauthorized firmware from being loaded. Which steps are typically involved in enabling secure boot on a BlueField-3 DPU, starting from a factory- default state? (Select TWO)

  • A. Update the DPU's BMC firmware to the latest version.
  • B. Flash the BlueField-3 DPIJ with a secure boot-enabled OS image that is signed with the enrolled keys.
  • C. Generate cryptographic keys and enroll them in the DPIJ's IJEFI firmware using tools provided by NVIDIA or Mellanox.
  • D. Install the latest version of the Mellanox OFED drivers on the host server.
  • E. Configure the server's BIOS to enable UEFI boot mode.

Answer: B,C

Explanation:
The crucial steps are generating and enrolling cryptographic keys in the DPIJ's UEFI firmware, which establishes the chain of trust, and flashing the DPIJ with a secure boot-enabled OS image signed with those keys. Without these, secure boot won't function correctly. While updating the BMC firmware (E) is generally a good practice, and enabling IJEFI boot mode in the server's BIOS (C) is necessary for UEFI in general, they are not specific steps for enabling secure boot on the DPIJ itself . Installing host drivers (A) is also irrelevant to secure boot.


NEW QUESTION # 120
You are building a cloud-native application that uses both CPU and GPU resources. You want to optimize resource utilization and cost by scheduling CPU-intensive tasks on nodes without GPUs and GPU-intensive tasks on nodes with GPUs. How would you achieve this node selection and workload placement in Kubernetes?

  • A. Use node affinity rules to schedule CPU-intensive tasks on nodes without GPUs and GPU-intensive tasks on nodes with GPUs.
  • B. Use node affinity rules to schedule CPU-intensive tasks on nodes with GPUs and GPU-intensive tasks on nodes without GPUs.
  • C. Use taints and tolerations to dedicate nodes without GPUs to CPU-intensive tasks and nodes with GPUs to GPU-intensive tasks.
  • D. Use labels to identify the CPU and GPU-intensive nodes.
  • E. Use resource quotas to limit the CPU resources available on nodes with GPUs and the GPU resources available on nodes without GPUs.

Answer: A

Explanation:
Node affinity rules allow you to specify constraints on which nodes a pod can be scheduled on. By using node affinity rules, you can ensure that CPU-intensive tasks are scheduled on nodes without GPUs and GPU-intensive tasks are scheduled on nodes with GPUs. This optimizes resource utilization and cost. Taints and tolerations can be used, but affinity is more flexible. Resource quotas limit resource usage but do not control placement.


NEW QUESTION # 121
After installing the NGC CLI, you attempt to run 'ngc config set' and encounter the following error: 'Error: API key is invalid or missing'.
What are the most likely causes of this issue and how can you resolve them?

  • A. The host does not have network access to NGC.
  • B. The NGC CLI is not properly installed. Reinstall the package using 'pip install -upgrade nvidia-cli'
  • C. The NGC API key is incorrect or has expired. Verify the API key in your NVIDIA account and update the configuration using 'ngc config set'.
  • D. The NGC CLI configuration file is corrupted. Delete the file (A.ngc/config.json') and reconfigure the CLI.
  • E. The NGC service is down. Check the NVIDIA NGC status page for any known outages.

Answer: A,C,D

Explanation:
The most likely cause is an invalid API key (B) or a corrupted configuration file (C), or the host lacks network access (E). Reinstalling the package (A) might not resolve the issue if the problem lies with the API key or config file. While NGC service outages (D) are possible, they are less common.


NEW QUESTION # 122
Consider an AI server equipped with two NVIDIAAI 00 GPUs interconnected with NVLink. You want to maximize the memory bandwidth available to a CUDA application. You observe that the application's performance doesn't scale linearly with the number of GPUs. Which of the following coding techniques or configurations could potentially improve inter-GPU memory access performance?

  • A. Employ Unified Memory (I-JM) with prefetching to automatically migrate data between GPUs as needed.
  • B. Manually manage data transfers between GPUs using 'cudaMemcpyPeer' to exploit NVLink bandwidth. Choose the GPU with more free memory for allocations.
  • C. Ensure all memory allocations are performed on GPU O to minimize data transfer.
  • D. Disable NVLink to force the application to use PCle, which might provide more consistent performance.
  • E. Use CUDA-aware MPl for inter-GPU communication to leverage NVLink.

Answer: B

Explanation:
ScudaMemcpyPeer allows explicit, optimized data transfers between GPUs using NVLink. Unified Memory with prefetching can simplify development, but might not always provide the best performance. CUDA-aware MPl is typically used for inter-node communication, not intra-node GPU-GPU. Allocating all memory on one GPU defeats the purpose of multi-GPU acceleration. PCle will be slower than NVLink. Manually managing memory transfers, while complex, gives the programmer the most control over leveraging NVLink bandwidth.


NEW QUESTION # 123
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