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NVIDIA AI Operations Sample Questions (Q39-Q44):

NEW QUESTION # 39
You are tuning the storage performance of a Kubernetes cluster that uses Longhorn as the storage backend. You've noticed inconsistent read latencies. What Longhorn specific configurations could you adjust to potentially improve the consistency and reduce latency for read operations?

A. Reduce the size of the Longhorn volumes to decrease the amount of data that needs to be read.
B. Adjust the 'replica-auto-balance' setting to redistribute replicas more evenly across nodes.
C. Disable Longhorn's built-in monitoring to reduce overhead.
D. Increase the number of replicas for each Longhorn volume to improve data redundancy and read parallelism.
E. Enable 'data locality' to ensure that replicas are preferably placed on the same node as the pod accessing the volume, reducing network latency.

Answer: B,D,E

Explanation:
More replicas increase read parallelism. 'replica-auto-balance' ensures replicas are well-distributed. 'data locality minimizes network hops for reads. It is not beneficial to reduce volume size, as that is required. Disabling built-in monitoring is also a bad idea.

NEW QUESTION # 40
You have deployed the NVIDIA Device Plugin for Kubernetes on your BCM-managed cluster. After a kernel update on one of the worker nodes, the device plugin fails to discover the GPUs. The error messages indicate a mismatch between the driver version expected by the device plugin and the actual driver version installed on the node. What is the MOST reliable way to resolve this issue without disrupting other workloads?

A. Uninstall and reinstall the NVIDIA Container Toolkit on the affected worker node to automatically update the driver version.
B. Use a DaemonSet to manage the NVIDIA driver installation on all worker nodes, ensuring a consistent driver version across the cluster and compatibility with the device plugin.
C. Manually downgrade the NVIDIA driver on the affected worker node to match the version expected by the device plugin.
D. Update the NVIDIA Device Plugin deployment manifest to specify the driver version installed on the node.
E. Remove the NVIDIA Device Plugin and replace it with the 'nvidia-driver-installer' helm chart

Answer: B

Explanation:
Using a DaemonSet to manage the NVIDIA driver installation is the MOST reliable and scalable solution. It ensures that all worker nodes have the correct driver version and simplifies driver updates. Manually downgrading or updating individual nodes (A, B) is not sustainable. Reinstalling the toolkit (D) might not update the driver. Simply removing and replacing the plugin (E) doesn't address driver mismatch and would likely use a similar deployment method that would lead to the same error.

NEW QUESTION # 41
You're running a Docker container with a deep learning model. While the model trains successfully, you observe that the GPU utilization fluctuates significantly, and the training process is slower than expected. What could be the cause and how would you address it?

A. The model is not large enough to saturate the GPU. Consider using a larger model or increasing the complexity of the computations.
B. The batch size is too small. Increase the batch size to improve GPU utilization, but be mindful of GPU memory limitations.
C. The data loading pipeline is a bottleneck. Optimize data loading by using asynchronous data loaders and prefetching data to the GPU.
D. There is CPU contention. Use 'taskset to bind the data loading process to specific CPU cores to reduce CPU switching overhead.
E. The Docker container is not receiving enough CPU resources. Increase the CPU shares or CPU quota allocated to the container.

Answer: A,B,C,D

Explanation:
Fluctuating GPU utilization suggests that the GPU is not being consistently fed with data or computations. Data loading bottlenecks (A), small batch sizes (B), or an under-utilizing model (C) can all cause this. CPU contention (D) may be the culprit.

NEW QUESTION # 42
You are deploying an AI application using Fleet Command. You want to ensure that the application automatically restarts if it crashes on an edge device. How can you achieve this?

A. Use Fleet Command's built-in health check and auto-restart features (if available and configured).
B. Disable the application's crash reporting to prevent crashes.
C. Manually monitor the application and restart it if it crashes.
D. Increase the memory allocated to the application to prevent crashes.
E. Configure a systemd service or similar process manager on the edge device to automatically restart the application.

Answer: A

Explanation:
Fleet Command's built-in features are the most integrated and manageable way to handle application restarts. Manual monitoring (A) is not scalable. Systemd (B) requires manual configuration on each device. Disabling crash reporting (D) hides issues. Increasing memory (E) might help but doesn't guarantee restarts.

NEW QUESTION # 43
You have a hybrid environment with some GPUs connected via NVLink and others connected via PCle. You want to use 'nvsm' to manage only the NVLink fabric. How can you configure 'nvsm' to ignore the PCle-connected GPUs?

A. Configure a whitelist in 'nvsm.conf to include only the NVLink devices by their NVLink IDs.
B. Configure a blacklist in 'nvsm.conf to exclude the PCle devices by their PCl IDs.
C. Use the 'nvsm -ignore-pcie' command-line option when starting the service.
D. Update the system BIOS to disable the PCle slots.
E. There is no way to configure 'nvsm' to ignore specific GPUs.

Answer: B

Explanation:
Typically, you can configure 'nvsm' to ignore specific GPUs by creating a blacklist in the 'nvsm.conf file. This blacklist would contain the PCI IDs of the PCIe-connected GPUs. 'nvsm' is designed to manage fabric links. 'nvsm' does not have a command line option to ignore PCle connected GPUs.

NEW QUESTION # 44
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