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[General] Free PDF Quiz 2026 NCP-AIO: High Pass-Rate NVIDIA AI Operations Valid Dump

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【General】 Free PDF Quiz 2026 NCP-AIO: High Pass-Rate NVIDIA AI Operations Valid Dump

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NVIDIA NCP-AIO Exam Syllabus Topics:
TopicDetails
Topic 1
  • Administration: This section of the exam measures the skills of system administrators and covers essential tasks in managing AI workloads within data centers. Candidates are expected to understand fleet command, Slurm cluster management, and overall data center architecture specific to AI environments. It also includes knowledge of Base Command Manager (BCM), cluster provisioning, Run.ai administration, and configuration of Multi-Instance GPU (MIG) for both AI and high-performance computing applications.
Topic 2
  • Troubleshooting and Optimization: NVIThis section of the exam measures the skills of AI infrastructure engineers and focuses on diagnosing and resolving technical issues that arise in advanced AI systems. Topics include troubleshooting Docker, the Fabric Manager service for NVIDIA NVlink and NVSwitch systems, Base Command Manager, and Magnum IO components. Candidates must also demonstrate the ability to identify and solve storage performance issues, ensuring optimized performance across AI workloads.
Topic 3
  • Installation and Deployment: This section of the exam measures the skills of system administrators and addresses core practices for installing and deploying infrastructure. Candidates are tested on installing and configuring Base Command Manager, initializing Kubernetes on NVIDIA hosts, and deploying containers from NVIDIA NGC as well as cloud VMI containers. The section also covers understanding storage requirements in AI data centers and deploying DOCA services on DPU Arm processors, ensuring robust setup of AI-driven environments.
Topic 4
  • Workload Management: This section of the exam measures the skills of AI infrastructure engineers and focuses on managing workloads effectively in AI environments. It evaluates the ability to administer Kubernetes clusters, maintain workload efficiency, and apply system management tools to troubleshoot operational issues. Emphasis is placed on ensuring that workloads run smoothly across different environments in alignment with NVIDIA technologies.

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NVIDIA AI Operations Sample Questions (Q47-Q52):NEW QUESTION # 47
You are managing a cluster with multiple nodes connected via NVLink and NVSwitch. After a network outage, some of the NVLink connections are showing as 'degraded' in 'nvsm show links'. What steps should you take to attempt to restore the connections to their optimal state? (Select TWO correct answers)
  • A. Run 'nvsm repair linkS on the affected nodes.
  • B. Reboot all nodes in the cluster simultaneously.
  • C. Update the BIOS on all servers.
  • D. Restart the 'nvsm' service on all nodes.
  • E. Check physical NVLink cable connections for damage or looseness.
Answer: D,E
Explanation:
Restarting the 'nvsm' service can help re-establish the connections. Checking the physical cable connections is crucial to ensure they are secure and undamaged. 'nvsm repair links' is not a valid command. Rebooting the entire cluster may be necessary in some situations, but it's a more disruptive step to take initially. A BIOS update is unlikely to solve the problem if it arose after a network outage.

NEW QUESTION # 48
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. Configure all MIG instances with equal memory and compute allocation to provide a fair distribution of resources.
  • B. 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.
  • C. Create multiple small MIG instances and distribute the HPC workload across them.
  • D. Disable MIG and allow the HPC application to utilize the entire GPU for maximum performance.
  • E. Create a single, large MIG instance dedicated solely to the HPC application, maximizing its compute capacity.
Answer: B
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 # 49
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 adjust the clock speeds of the GPUs to maximize performance for this workload.
  • B. Use BCM to monitor the network bandwidth between the GPU nodes and the storage system.
  • C. Use BCM to migrate the workload to a different GPU node with more available resources.
  • D. Use BCM to profile the workload's GPU usage and identify specific kernels or operations that are consuming the most GPU time.
  • E. Use BCM to restart the Docker container running the workload.
Answer: B,D
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 # 50
You're setting up a Kubernetes cluster on NVIDIA DGX servers using Bare Metal Container (BCM). During the pre-flight checks, the 'kubelet' fails to start on one of the worker nodes. The logs indicate a problem with device plugin registration. Which of the following is the MOST likely cause and the best initial troubleshooting step?
  • A. Missing or misconfigured NVIDIA Container Toolkit. Ensure the toolkit is installed and configured correctly on the worker node.
  • B. Incorrect NVIDIA driver version. Verify the driver version is compatible with the Kubernetes version and NVIDIA Container Toolkit.
  • C. SELinux policy preventing the device plugin from accessing the GPU devices. Check SELinux logs and adjust policies accordingly.
  • D. Firewall blocking communication between the kubelet and the NVIDIA device plugin. Check firewall rules on the worker node.
  • E. Insufficient CPU resources allocated to the kubelet. Increase the CPU limit for the kubelet process.
Answer: A
Explanation:
The NVIDIA Container Toolkit is essential for exposing GPU devices to containers within Kubernetes. A missing or misconfigured toolkit is the most common reason for device plugin registration failures. Checking its installation and configuration is the crucial first step. Incorrect driver version (A) could be an issue but less likely. Firewall (B) and SELinux (C) are also possibilities, but Toolkit (D) is most direct. CPU resources (E) are unlikely to cause device registration issues.

NEW QUESTION # 51
A user reports that they are unable to submit jobs to a specific partition. You've verified that the partition exists and is enabled. What are the possible reasons for this?
  • A. The 'MaxNodes' parameter for the partition is set to 0.
  • B. The partition's state is set to INACTIVE.
  • C. The user's account is not associated with the partition.
  • D. The user has exceeded their QOS limit.
  • E. All of the above
Answer: E
Explanation:
All the options are reasons for the user to be unable to submit jobs to a specific partition. All must be checked to solve the root problem.

NEW QUESTION # 52
......
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