MARVELOUS DOWNLOAD NCP-AIO FREE DUMPS - FIND SHORTCUT TO PASS NCP-AIO EXAM

Marvelous Download NCP-AIO Free Dumps - Find Shortcut to Pass NCP-AIO Exam

Marvelous Download NCP-AIO Free Dumps - Find Shortcut to Pass NCP-AIO Exam

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NVIDIA AI Operations Sample Questions (Q72-Q77):

NEW QUESTION # 72
You are experiencing inconsistent performance across different GPUs in your NVLink fabric. You suspect that some NVLink connections may be operating at a lower bandwidth than expected. How can you verify the actual bandwidth of each NVLink connection using ' nvsm' or related tools?

  • A. Check the GPU temperature using 'nvidia-smi'.
  • B. Use 'nvsm show linkS and check the reported 'link speed'.
  • C. Monitor the network interface statistics using 'ifconfig' .
  • D. Run a benchmark tool like 'nvidia-smi nvlink -capcheck' to measure the achieved bandwidth.
  • E. Use 'nvsm show topology' and check the reported 'link width'.

Answer: D

Explanation:
While 'nvsm show linkS reports the configured link speed, it doesn't show the actual achieved bandwidth. A benchmark tool specifically designed for NVLink (similar to 'nvidia-smi nvlink -capcheck' , though the exact command might vary) is necessary to measure the actual bandwidth achieved on each connection. The other options provide unrelated information.


NEW QUESTION # 73
An AI research team requires access to GPU resources for both training and inference tasks. You are responsible for configuring the NVIDIA A100 GPUs using MIG. The training task requires high memory bandwidth, while the inference tasks require low latency. How would you configure MIG to best satisfy both workloads simultaneously?

  • A. Create MIG instances with the same memory and compute allocation for both training and inference.
  • B. Create a single large MIG instance and allocate it dynamically between training and inference.
  • C. Create a MIG instance with high memory bandwidth for training and a MIG instance with optimized compute for low-latency inference.
  • D. Create a large MIG instance for training and a small MIG instance for inference.
  • E. Don't use MIG at all, and schedule tasks based on priority.

Answer: C

Explanation:
By creating MIG instances tailored to the specific needs of each task high memory bandwidth for training and optimized compute for low-latency inference you ensure optimal performance for both workloads. Options A and B may not fully address the specific needs of the training and inference tasks. Option D is not suitable because dynamic allocation may introduce latency and complicate resource management. Option E means there will be resource contention and is bad.


NEW QUESTION # 74
You want to configure a Slurm cluster with heterogeneous nodes, some equipped with high-performance GPUs and others with only CPUs. Which Slurm configuration parameter allows you to define and utilize these different node types effectively?

  • A. QOS=gpu
  • B. Feature=gpu Feature=cpu
  • C. NodeName=node[1-10] Gres=gpu:2 NodeName=node[11-20]
  • D. TRES= gpu:2
  • E. PartitionName=gpu PartitionName=cpu

Answer: C

Explanation:
Using NodeName and Gres (Generic Resources), you can specify the resources available on each node. In this example, nodes 1- 10 are configured with 2 GPUs each, while nodes 11-20 are assumed to have only CPUs (no Gres specified).


NEW QUESTION # 75
In a data center designed for AI, what is the primary benefit of using GPU virtualization technologies like NVIDIA vGPU?

  • A. To increase the number of physical GPUs that can be installed in a server.
  • B. To simplify the deployment of AI applications on bare metal servers.
  • C. To eliminate the need for high-bandwidth networking.
  • D. To improve GPU utilization by allowing multiple virtual machines to share a single physical GPU.
  • E. To reduce the overall power consumption of the data center.

Answer: D

Explanation:
GPU virtualization allows for better resource utilization by dividing a physical GPU among multiple VMs, improving efficiency and reducing costs. While power consumption can be indirectly affected by more efficient resource allocation, that's not the primary benefit.


NEW QUESTION # 76
You are deploying BCM on a Kubernetes cluster that utilizes a custom ingress controller What configuration changes might be necessary to ensure external access to the BCM web interface?

  • A. Configure the ingress controller to forward traffic to the BCM service on the appropriate port (typically 3000).
  • B. Update the BCM Helm chart to automatically configure the ingress controller.
  • C. Disable the default ingress controller and use the custom ingress controller exclusively.
  • D. Modify the BCM service type to 'LoadBalancer'.
  • E. Create a custom resource definition (CRD) for BCM in the ingress controller.

Answer: A

Explanation:
The primary configuration change required is to configure the ingress controller to forward traffic to the BCM service. This typically involves creating an ingress resource that defines the hostname and path for accessing the BCM web interface and maps it to the BCM service on the correct port (usually 3000). Changing the service type to 'LoadBalancer' might work, but it's less flexible and might not be compatible with all ingress controllers. Custom CRDs and disabling the default controller are generally unnecessary.


NEW QUESTION # 77
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