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NVIDIA AI Operations Sample Questions (Q44-Q49):

NEW QUESTION # 44
You have deployed a VMI container with Triton Inference Server on a cloud provider that supports MIG (Multi-lnstance GPU). You have a single A100 GPU and you want to partition it into two MIG instances to serve two different models concurrently, each requiring half of the GPU's resources. What steps are necessary to achieve this?

A. No special configuration is needed; Triton automatically detects and utilizes MIG instances.
B. MIG is not a supported feature in Triton
C. Partition the AIOO GPU into two MIG instances using the 'nvidia-smi' command-line tool, then configure Triton to use each MIG instance separately by specifying the corresponding UUIDs in the model configuration files.
D. Bake different drivers in Triton Container to target different MIG instances
E. Configure the cloud provider's instance settings to automatically partition the GPU into MIG instances.

Answer: C

Explanation:
To utilize MIG with Triton, you need to first partition the GPU into MIG instances using 'nvidia-smi' , and then configure Triton to use each MIG instance separately. This involves specifying the correct UUIDs for each MIG instance in the model configuration files, allowing Triton to isolate and utilize each partition effectively.

NEW QUESTION # 45
Your application, which relies heavily on NVLink for inter-GPU communication, is experiencing performance degradation over time. After investigating, you suspect that NVLink link errors are accumulating. How can you proactively monitor NVLink link error counts and trigger an alert when they exceed a predefined threshold? (Select TWO correct answers)

A. Analyze the system's kernel log for NVLink-related error messages.
B. Implement a custom script that periodically reboots the GPUs to clear the error counters.
C. Use 'nvidia-smi' to query NVLink error counters and integrate the output into a monitoring system (e.g., Prometheus, Grafana).
D. Configure 'nvsm' to automatically restart the NVLink connections when errors are detected.
E. Use 'nvsm show links' and parse the output to extract error counts, then integrate this into a monitoring system.

Answer: C,E

Explanation:
'nvsm show linkS (or a similar 'nvsrn' command) and 'nvidia-smr are both capable of providing NVLink error counts. The key is to then integrate the output of these commands into a monitoring system that can trigger alerts based on predefined thresholds. 'nvsm' doesn't have native auto-restart features for links based on errors. Periodically rebooting GPUs is a poor workaround. Kernel logs can provide some information, but it is not an effective way of real time monitoring.

NEW QUESTION # 46
What is the primary purpose of assigning a provisioning role to a node in NVIDIA Base Command Manager (BCM)?

A. To allow the node to manage software images and provision other nodes
B. To configure the node as a container orchestration manager
C. To assign the node as a storage manager for certified storage
D. To enable the node to monitor GPU utilization across the cluster

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In NVIDIA Base Command Manager (BCM), assigning theprovisioning roleto a node enables that node to manage software images and perform provisioning tasks for other nodes in the cluster. This role allows automated deployment and configuration of cluster nodes, ensuring consistency and simplifying large-scale management. It is not primarily responsible for container orchestration, GPU monitoring, or storage management.

NEW QUESTION # 47
You're managing a cluster that uses Kubernetes and the NVIDIA Device Plugin. A pod requests a GPU using resource limits. The pod starts, but the application within the pod reports that no GPUs are available. What troubleshooting steps should you take FIRST?

A. Restart the Kubernetes kubelet service on the node.
B. Inspect the Kubernetes node's status using 'kubectl describe node to ensure that the node is reporting GPU resources as available.
C. Verify that the NVIDIA Device Plugin is correctly installed and running on the Kubernetes nodes using 'kubectl describe daemonset nvidia-device-plugin-daemonset -n kube-system'.
D. Reinstall the NVIDIA drivers on the host.
E. Check the pod's YAML definition to ensure that the GPU resource request is correctly specified (e.g., 'nvidia.com/gpu: 1

Answer: B,C,E

Explanation:
The initial steps should focus on verifying the correct setup of the NVIDIA Device Plugin (A), ensuring the pod correctly requests GPU resources (B), and confirming that Kubernetes recognizes the GPU resources on the node (C). Restarting the kubelet (D) or reinstalling drivers (E) are more drastic measures that should be considered after confirming the basic configuration.

NEW QUESTION # 48
A user is running a large language model (LLM) training job on a multi-GPU server. The job utilizes PyTorch's 'DistributedDataParallel' (DDP). The training process seems to hang intermittently. How can you troubleshoot this issue using system management tools?

A. Use 'perf to profile the CPU usage of the PyTorch processes.
B. Examine the system logs C/var/log/syslog' or similar) for any error messages related to network or GPU communication.
C. Use 'nvidia-smi' to check GPU utilization and memory usage to identify potential imbalances across GPUs.
D. Monitor network bandwidth utilization between GPUs using 'ethtoor.
E. Run 'iostat' to observe disk 1/0 activity, as it might be causing delays.

Answer: B,C,D

Explanation:
DDP relies on efficient inter-GPU communication. Network bottlenecks (A) can cause hangs. GPU utilization imbalances (B) can lead to some GPUs waiting for others. System logs (C) might contain error messages indicating communication failures. While CPU profiling (D) and disk I/O monitoring (E) might be useful in other scenarios, they are less likely to be the primary cause of hangs in DDP training.

NEW QUESTION # 49
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