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NVIDIA AI Infrastructure Sample Questions (Q39-Q44):
NEW QUESTION # 39
You're deploying a large language model for inference using NVIDIA Triton Inference Server. You need to validate that the server can handle the expected query load while maintaining acceptable latency. Which tools and metrics are most relevant for this validation?
Answer: C
Explanation:
Validating Triton's performance requires a comprehensive approach. Monitoring GPU usage with 'nvidia-smi', tracking Triton's internal metrics, using load testing tools to simulate real-world traffic, and monitoring CPU utilization are all important for identifying bottlenecks and ensuring optimal performance.
NEW QUESTION # 40
You are replacing a faulty NVIDIA Tesla V 100 GPU in a server. After physically installing the new GPU, the system fails to recognize it. You've verified the power connections and seating of the card. Which of the following steps should you take next to troubleshoot the issue?
Answer: A,C
Explanation:
After verifying the physical installation, the next steps are to ensure the system's firmware is up-to-date and that the correct drivers are installed. Older BIOS/BMC firmware may not properly recognize newer GPUs, and incorrect drivers will prevent the GPU from functioning correctly. RMAing the new GPU or reinstalling the OS prematurely are inefficient troubleshooting steps. The system BIOS may have an option to disable and enable the GPU slot, but that would be rare.
NEW QUESTION # 41
A customer reports that after installing an additional NVIDIA GPU into their existing AI server, they are experiencing IOMMU related errors during VM startup that leverage GPU pass-through. The error message indicates a problem with address translation. Which troubleshooting steps should be taken? (Select TWO)
Answer: B,C
Explanation:
IOMMU errors often stem from incorrect configuration or outdated firmware. Disabling IOMMU is generally not recommended as it reduces security. Ensuring IOMMIJ is enabled in the BIOS/UEFI and kernel command line is crucial. Updating the BIOS/IJEFI addresses potential bugs in IOMMU implementation. While memory allocation could be a factor, it is not the most likely immediate cause. Reinstalling drivers within the VM is less likely to resolve IOMMU-related errors.
NEW QUESTION # 42
You are tasked with upgrading the NVIDIA driver on a Kubernetes node hosting GPU-accelerated A1 workloads. To minimize downtime and ensure a smooth transition, which sequence of steps should you follow?
Answer: C
Explanation:
Cordoning the node prevents new pods from being scheduled on it. After upgrading the driver and rebooting, uncordoning the node allows Kubernetes to resume scheduling workloads. Draining the node before upgrading can cause unnecessary downtime if pods are migrated before the upgrade process starts. The NVIDIA container toolkit must be compatible to the NVIDIA driver, but the upgrade sequence follows Option C steps.
NEW QUESTION # 43
You are using MIG (Multi-lnstance GPU) on an NVIDIAAI 00 GPU within a Kubernetes cluster. You want to configure a pod to use a specific MIG instance. How do you define the GPU resource request in the pod's YAML definition?
Answer: A
Explanation:
When using MIG, you need to specify the MIG device profile name in the resource request to allocate a specific MIG instance to the pod. For example, 'nvidia.com/mig-1g.7gb: 1' requests one instance of the lg.7gb MIG profile. Specifying the full GPU ID will not work with MIG. Generic resource requests do not work with MIG.
NEW QUESTION # 44
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