NVIDIA NCP-AIO DUMPS OBTAIN EXAM RESULTS SIMPLY 2025

NVIDIA NCP-AIO Dumps Obtain Exam Results Simply 2025

NVIDIA NCP-AIO Dumps Obtain Exam Results Simply 2025

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NVIDIA AI Operations Sample Questions (Q54-Q59):

NEW QUESTION # 54
When installing BCM, which of the following authentication methods can be configured to secure access to the BCM web interface?

  • A. Kerberos
  • B. OAuth 2.0
  • C. LDAP (Lightweight Directory Access Protocol)
  • D. NTLM
  • E. Local User Accounts

Answer: B,C,E

Explanation:
BCM supports Local User Accounts for basic authentication, LDAP for integration with existing directory services, and OAuth 2.0 for modern authentication patterns. Kerberos and NTLM are not directly supported authentication methods for the BCM web interface.


NEW QUESTION # 55
You need to implement a highly available and fault-tolerant Fleet Command deployment for a mission-critical AI application. What architectural considerations are MOST important for ensuring resilience?

  • A. Rely solely on the edge devices' ability to operate independently during Fleet Command outages.
  • B. Deploy a single Fleet Command server in a standard configuration.
  • C. Monitor the Fleet Command server's performance and resource utilization.
  • D. Utilize a multi-node Fleet Command cluster with redundancy and failover mechanisms, combined with geographically diverse edge device deployments.
  • E. Regularly back up the Fleet Command server's configuration and data.

Answer: D

Explanation:
A multi-node cluster provides redundancy and failover capabilities, ensuring high availability. Geographically diverse edge deployments minimize the impact of regional outages. A single server (A) is a single point of failure. Backups (C) are important but don't prevent downtime. Monitoring (D) helps identify issues but doesn't ensure resilience. Solely relying on edge devices (E) limits manageability and control.


NEW QUESTION # 56
You are deploying a multi-GPU training job using a container from NGC on a Slurm cluster. The container expects the number of GPUs to be available in the 'CUDA VISIBLE DEVICES' environment variable. How do you ensure this variable is correctly set within the Slurm job script?

  • A. Configure the NVIDIA Container Toolkit to automatically detect and set 'CUDA VISIBLE DEVICES'.
  • B. Set the environment variable manually in the Slurm job script to a fixed value (e.g.,
  • C. Define the "CUDA VISIBLE DEVICES' environment variable in the containers Docket-file.
  • D. Utilize the Slurm environment variable 'SLURM JOB GPUS' to dynamically set 'CUDA_VISIBLE DEVICES' in the job script (e.g., 'export
  • E. Use the Slurm command 'srun' with the '-gpus' option to allocate GPUs and automatically set

Answer: D,E

Explanation:
B and D are correct. 'srun -gpus' handles GPU allocation and sets the environment variable. 'SLURM JOB GPUS provides a dynamic way to access allocated GPUs within the script. A is incorrect as it doesn't adapt to the actual allocation. C is incorrect because it's not a Slurm configuration. E depends on the specific toolkit version and might not be reliable without explicit configuration in the job script.


NEW QUESTION # 57
You have configured MIG instances for different users in a multi-tenant environment. One user complains that their application is running slower than expected, despite having a dedicated MIG instance. You suspect resource contention on the host system. Which of the following could be causing the slowdown, even with MIG in place?

  • A. Insufficient host memory. The overall host system might be running low on memory, causing swapping and slowing down all processes.
  • B. MIG guarantees complete isolation, so resource contention is impossible.
  • C. Network bandwidth limitations. If the application relies on network communication, bandwidth limitations could be the bottleneck.
  • D. Insufficient power provided by the PSU.
  • E. CPU core oversubscription. Even with dedicated MIG instances, CPU cores might be oversubscribed, leading to performance degradation.

Answer: A,C,E

Explanation:
MIG provides GPU resource isolation, but it does not isolate other system resources. CPU oversubscription, insufficient host memory, and network bandwidth limitations can all contribute to performance slowdowns, even with dedicated MIG instances. It's important to monitor and manage these resources in addition to GPU resources.


NEW QUESTION # 58
A user complains that their AI training job is running very slowly. Upon investigation, you discover that the pod is scheduled onto a node with a slow network connection, causing significant delays in data transfer. How would you ensure that future similar jobs are scheduled onto nodes with faster network connections?

  • A. Use inter-pod affinity to force the job onto nodes already running network-intensive workloads.
  • B. Increase the resource requests for the pod to trigger rescheduling.
  • C. Configure the kubelet to prioritize pods based on their network usage.
  • D. Implement node affinity rules based on network bandwidth labels, and label the nodes appropriately.
  • E. Manually reschedule the pod onto a node with a faster network.

Answer: D

Explanation:
The correct answer is B. By labeling nodes with their network bandwidth capabilities (e.g., 'network-bandwidth: 100GbpS), you can then use node affinity rules in your pod specifications to ensure that jobs requiring high bandwidth are scheduled onto suitable nodes. Option A is a temporary fix. Options C and D do not address the core issue of network bandwidth. Option E would exacerbate the problem by concentrating network-intensive workloads on the same nodes.


NEW QUESTION # 59
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