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Valid Test NCP-AIO Tutorial, NCP-AIO Certification

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NVIDIA AI Operations Sample Questions (Q42-Q47):

NEW QUESTION # 42
You have noticed that users can access all GPUs on a node even when they request only one GPU in their job script using --gres=gpu:1. This is causing resource contention and inefficient GPU usage.
What configuration change would you make to restrict users' access to only their allocated GPUs?

A. Modify the job script to include additional resource requests for CPU cores alongside GPUs.
B. Increase the memory allocation per job to limit access to other resources on the node.
C. Enable cgroup enforcement in cgroup.conf by setting ConstrainDevices=yes.
D. Set a higher priority for Jobs requesting fewer GPUs, so they finish faster and free up resources sooner.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To restrict users' access strictly to the GPUs allocated to their jobs, Slurm usescgroups (control groups)for resource isolation. Enabling device cgroup enforcement by settingConstrainDevices=yesincgroup.
confenforces device access restrictions, ensuring jobs cannot access GPUs beyond those assigned.
* Increasing memory allocation or setting job priorities does not restrict device access.
* Modifying job scripts to request additional CPU cores does not limit GPU access.
Hence, enablingcgroup enforcement with ConstrainDevices=yesis the correct method to prevent users from accessing unallocated GPUs.

NEW QUESTION # 43
Consider an HPC application heavily reliant on CODA. You plan to leverage MIG to optimize GPU resource allocation within your cluster.
Which configuration approach would BEST ensure the HPC application benefits from high GPU compute capability while coexisting with other workloads?

A. Create a single, large MIG instance dedicated solely to the HPC application, maximizing its compute capacity.
B. Configure all MIG instances with equal memory and compute allocation to provide a fair distribution of resources.
C. Create MIG instances tailored to the HPC application's specific memory and compute needs, allocating the necessary resources without over-provisioning. Utilize the remaining resources for other workloads.
D. Create multiple small MIG instances and distribute the HPC workload across them.
E. Disable MIG and allow the HPC application to utilize the entire GPU for maximum performance.

Answer: C

Explanation:
Tailoring MIG instances to the HPC application's specific requirements ensures efficient resource allocation and allows other workloads to utilize the remaining GPU capacity. D is not ideal for concurrent workloads. A and E don't account for specific workload requirements.

NEW QUESTION # 44
You want to upgrade the NVIDIA drivers on your Kubernetes nodes without disrupting the running AI workloads. What is the recommended approach to perform a rolling upgrade of the NVIDIA drivers?

A. Upgrade the drivers on a single node and then propagate the changes to other nodes using a script.
B. Simultaneously upgrade the drivers on all nodes.
C. Drain each node, upgrade the drivers, and then uncordon the node.
D. Delete all pods on the node, upgrade the drivers, and then recreate the pods.
E. Use a Kubernetes DaemonSet to manage the driver installation and updates, ensuring a rolling update strategy.

Answer: C,E

Explanation:
The correct answers are A and E. Draining a node Ckubectl drain') gracefully evicts pods from the node before upgrading the drivers, and then uncordoning it ('kubectl uncordori) makes it available for scheduling again. Alternatively, a DaemonSet can manage the driver installation and updates, as a rolling upgrade strategy by design will restart pods one by one, ensuring minimum disruption. Options B and C cause downtime. Option D might work, but is not automated and thus not a best practice.

NEW QUESTION # 45
You are experiencing performance issues with a specific AI workload running on your Kubernetes cluster managed by BCM. BCM shows high GPU utilization for this workload. How can you use BCM to further investigate the cause of the performance bottleneck?

A. Use BCM to profile the workload's GPU usage and identify specific kernels or operations that are consuming the most GPU time.
B. Use BCM to restart the Docker container running the workload.
C. Use BCM to monitor the network bandwidth between the GPU nodes and the storage system.
D. Use BCM to migrate the workload to a different GPU node with more available resources.
E. Use BCM to adjust the clock speeds of the GPUs to maximize performance for this workload.

Answer: A,C

Explanation:
BCM's integration with profiling tools allows you to analyze the workload's GPU usage and identify performance bottlenecks. You can also monitor the network bandwidth, as data transfer bottlenecks can significantly impact AI workload performance. While migrating the workload might help, understanding the bottleneck first is crucial. Adjusting clock speeds can be risky. Restarting the container is a general troubleshooting step but doesn't provide specific insights.

NEW QUESTION # 46
You are using 'nvsm' to manage your NVLink fabric. You want to verify the link speed and status between two specific GPUs. Which nvsm' command provides the MOST detailed information about individual NVLink connections?

A. nvsm show devices'
B. 'nvsm show configuration'
C. 'nvsm show health'
D. nvsm show links'
E. 'nvsm show topology'

Answer: D

Explanation:
'nvsm show links' provides detailed information about the individual NVLink connections, including their speed, status, and error counts. 'nvsm show topology' provides a high-level overview, while the other commands focus on different aspects of the system.

NEW QUESTION # 47
......

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