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Free NVIDIA NCP-AIO Exam Dumps - NCP-AIO Certification Cost

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NVIDIA AI Operations Sample Questions (Q21-Q26):

NEW QUESTION # 21
After a power outage, the 'nvsm' service fails to start, and the logs indicate a database corruption error. What steps should you take to recover the nvsm' database? (Select TWO correct answers)

A. Attempt to repair the database using 'nvsm --repair-db' (if the command exists).
B. Delete the database files and restart 'nvsm' (which will create a new, empty database).
C. Format all disks on the system.
D. Restore the database from a backup, if available.
E. Reinstall 'nvsm' .

Answer: B,D

Explanation:
The ideal solution is to restore from a backup. If no backup is available, deleting the database files will force 'nvsm' to create a new one. A database corruption error generally means data inconsistency. While 'nvsm -repair-db' is a plausible (but hypothetical) command, It is often safer and quicker just to delete and restart, than repair. Formatting all disks is a drastic and unnecessary measure. Reinstalling the 'nvsm' service won't fix the database corruption itself, it will still point to the corrupted DB files.

NEW QUESTION # 22
Which network topology is generally preferred for AI training workloads in a data center, emphasizing low latency and high bandwidth between GPU servers?

A. A single IOGbE switch connecting all nodes.
B. Token Ring.
C. Clos network (e.g., Fat-Tree) with RoCEv2 or InfiniBand.
D. Ethernet with link aggregation (LAG) only.
E. Spanning Tree Protocol (STP) based Ethernet.

Answer: C

Explanation:
Clos networks, particularly Fat-Tree topologies utilizing RoCEv2 or InfiniBand, provide the necessary low latency and high bandwidth for efficient inter-GPU communication during distributed training. STP based Ethernet is unsuitable due to its blocking nature and potential for high latency. LAG helps but doesn't provide the full benefits of a Clos network.

NEW QUESTION # 23
In a data center designed for AI, what is the primary benefit of using GPU virtualization technologies like NVIDIA vGPU?

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

Answer: C

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 # 24
You are managing a Slurm cluster with multiple GPU nodes, each equipped with different types of GPUs.
Some jobs are being allocated GPUs that should be reserved for other purposes, such as display rendering.
How would you ensure that only the intended GPUs are allocated to jobs?

A. Use nvidia-smi to manually assign GPUs to each job before submission.
B. Verify that the GPUs are correctly listed in both gres.conf and slurm.conf, and ensure that unconfigured GPUs are excluded.
C. Increase the number of GPUs requested in the job script to avoid using unconfigured GPUs.
D. Reinstall the NVIDIA drivers to ensure proper GPU detection by Slurm.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Slurm GPU resource management, thegres.conffile defines the available GPUs (generic resources) per node, whileslurm.confconfigures the cluster-wide GPU scheduling policies. To prevent jobs from using GPUs reserved for other purposes (e.g., display rendering GPUs), administrators must ensure that only the GPUs intended for compute workloads are listed in these configuration files.
* Properly configuringgres.confallows Slurm to recognize and expose only those GPUs meant for jobs.
* slurm.confmust be aligned to exclude or restrict unconfigured GPUs.
* Manual GPU assignment usingnvidia-smiis not scalable or integrated with Slurm scheduling.
* Reinstalling drivers or increasing GPU requests does not solve resource exclusion.
Thus, the correct approach is to verify and configure GPU listings accurately ingres.confandslurm.confto restrict job allocations to intended GPUs.

NEW QUESTION # 25
You're implementing a preemption policy in your Slurm cluster to allow higher-priority jobs to interrupt lower-priority jobs. Which Slurm configuration parameters are MOST relevant to configure preemption? (Select TWO)

A. PreemptMode
B. FastSchedule
C. AccountingStorageType
D. PreemptType
E. SchedulerRootFilter

Answer: A,D

Explanation:
'PreemptMode' defines when preemption is triggered (e.g., 'OFF', 'CANCEL', 'REQUEUE'). 'preemptType' determines which jobs are eligible for preemption (e.g., 'priority', 'qos').

NEW QUESTION # 26
......

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