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NCP-AIO Exam Format - NCP-AIO Valid Braindumps Questions

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NCP-AIO Actual Lab Questions: NVIDIA AI Operations & NCP-AIO Study Guide

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NVIDIA AI Operations Sample Questions (Q13-Q18):

NEW QUESTION # 13
Consider the following code snippet using NVSHMEM:
If this program hangs indefinitely after 'nvshmem_barrier all()' inside the 'if (my_pe 0)' block, what is the MOST likely cause?

A. Incorrect use of 'CUDA VISIBLE DEVICES'
B. PE 0 is waiting for other PEs to complete within the loop, but other PEs have already finished and finalized.
C. Incorrect use of 'MPl Barrier'.
D. Deadlock due to insufficient NVLink bandwidth.
E. Memory corruption in 'nvshmem_malloc' .

Answer: B

Explanation:
The issue is a classic synchronization problem. PE 0 is the only PE entering the loop, and it calls to get the values from other PEs. However, the other PEs have already proceeded past the first , executed , and potentially even before PE 0 attempts to read their values. Thus, PE 0 is waiting for PEs that are no longer in a state to respond. MPI_Barrier isn't the issue, nor is NVLink bandwidth. Memory corruption or CUDA_VISIBLE_DEVICES issues would likely cause crashes, not hangs at this specific point.

NEW QUESTION # 14
A system administrator needs to lower latency for an AI application by utilizing GPUDirect Storage.
What two (2) bottlenecks are avoided with this approach? (Choose two.)

A. PCIe
B. DPU
C. NIC
D. CPU
E. System Memory

Answer: D,E

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
GPUDirect Storage allows data to be transferred directly from storage to GPU memory,bypassing the CPU and system memory. This reduces latency and overhead by avoiding data movement through the CPU and main memory, accelerating data feeding to GPUs for AI workloads. PCIe and NIC are still involved in the data path, and the DPU may participate depending on architecture but are not the primary bottlenecks avoided by GPUDirect Storage.

NEW QUESTION # 15
What is the primary benefit of using GPUDirect Storage (GDS) in an AI data center?

A. Simplified storage management through centralized control.
B. Enhanced data security with end-to-end encryption.
C. Increased storage capacity by compressing data on the fly.
D. Reduced CPU utilization during data transfers from storage to GPUs.
E. Automatic data tiering based on access frequency.

Answer: D

Explanation:
GPUDirect Storage allows data to be transferred directly from storage to GPU memory, bypassing the CPU and system memory. This reduces CPU utilization and improves overall performance, particularly for large datasets.

NEW QUESTION # 16
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. Manually downgrade the NVIDIA driver on the affected worker node to match the version expected by the device plugin.
B. Update the NVIDIA Device Plugin deployment manifest to specify the driver version installed on the node.
C. Uninstall and reinstall the NVIDIA Container Toolkit on the affected worker node to automatically update the driver version.
D. 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.
E. Remove the NVIDIA Device Plugin and replace it with the 'nvidia-driver-installer' helm chart

Answer: D

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 # 17
You've deployed a container from NGC on a Kubernetes cluster, but the application is experiencing intermittent GPU errors. You suspect memory leaks within the container are causing the issue. What is the most effective method to diagnose this problem?

A. Analyze the application's logs for CUDA error messages related to memory allocation.
B. Use NVIDIA Nsight Systems to profile the application and identify memory allocation patterns.
C. Restart the container regularly to clear potential memory leaks.
D. Monitor the container's CPU usage using 'kubectl top pod'.
E. Use 'nvidia-smi' within the container to monitor GPU memory usage over time.

Answer: A,B,E

Explanation:
B, C, and E are correct. 'nvidia-smi' provides real-time GPU memory usage. Application logs often contain CUDA errors indicating memory issues. Nsight Systems offers detailed profiling to pinpoint memory leaks. A is not relevant to GPU memory leaks. D is a workaround, not a diagnostic solution.

NEW QUESTION # 18
......

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