Practical NCP-AIO Information | NCP-AIO Valid Braindumps Sheet

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NVIDIA AI Operations Sample Questions (Q47-Q52):

NEW QUESTION # 47
A system administrator of a high-performance computing (HPC) cluster that uses an InfiniBand fabric for high-speed interconnects between nodes received reports from researchers that they are experiencing unusually slow data transfer rates between two specific compute nodes. The system administrator needs to ensure the path between these two nodes is optimal.
What command should be used?

A. ibstatus
B. ibnetdiscover
C. ibping
D. ibtracert

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To verify the optimal communication path and diagnose issues between two nodes in an InfiniBand fabric, theibtracertcommand is used. It traces the route that InfiniBand packets take through the fabric, identifying each hop and any potential bottlenecks or faulty links along the path.
* ibstatusprovides status information about local InfiniBand devices and ports.
* ibpingtests connectivity and latency between nodes.
* ibnetdiscoverdiscovers and prints the topology of the InfiniBand fabric but does not trace specific paths.
Therefore,ibtracertis the appropriate tool for path optimization verification between two compute nodes.

NEW QUESTION # 48
Your BCM pipeline uses a custom CUDA kernel. After upgrading the NVIDIA driver, the kernel fails to compile with an obscure error.
What is the MOST likely cause and how do you resolve it?

A. The driver upgrade corrupted the CUDA runtime. Reinstall the CUDA toolkit.
B. The custom kernel has a bug. Debug the kernel using cuda-gdb.
C. A and D
D. The CUDA toolkit version is incompatible with the new driver. Recompile the kernel with a compatible toolkit.
E. The new driver introduced a change in CUDAAPI. Update the kernel code to reflect the API change.

Answer: C

Explanation:
Driver upgrades can introduce CUDA toolkit incompatibilities or changes in the CUDAAPI that necessitate recompilation or code modification.

NEW QUESTION # 49
You are deploying a Deep Learning model as a service using Triton Inference Server within a VMI container on Google Cloud Platform (GCP). You want to leverage TensorRT for optimized inference performance. Which of the following steps are necessary to ensure TensorRT is properly configured and used?

A. Specify the 'optimization' parameter as 'TRT in the model configuration file Cconfig.pbtxt) for Triton.
B. No special configuration is needed; Triton automatically detects and uses TensorRT if available.
C. Ensure the Triton Inference Server container image includes the necessary TensorRT libraries and dependencies.
D. Convert the model to a TensorRT engine using 'trtexec' command before loading it into Triton.
E. Configure the GCP instance with an NVIDIA GPU that supports TensorRT.

Answer: A,C,D,E

Explanation:
To leverage TensorRT with Triton, you need to ensure the container has the libraries, convert the model to a TensorRT engine, specify the optimization parameter in the model configuration, and use a GPU that supports TensorRT. Each of these steps is crucial for proper TensorRT utilization.

NEW QUESTION # 50
An administrator is troubleshooting issues with NVIDIA GPUDirect storage and must ensure optimal data transfer performance.
What step should be taken first?

A. Check for compatible RDMA-capable network hardware and configurations.
B. Increase the GPU's core clock frequency.
C. Upgrade the CPU to a higher clock speed.
D. Install additional GPU memory (VRAM).

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
GPUDirect Storage performance relies heavily onRDMA-capable network hardware and proper configurationto enable direct memory access between storage and GPUs, bypassing CPU involvement for faster data transfers. Therefore, the first troubleshooting step should be to verify that RDMA-capable hardware is present and correctly configured. Adjusting GPU clocks, CPU speed, or GPU memory does not address the fundamental networking requirement for GPUDirect Storage.

NEW QUESTION # 51
A BCM pipeline deployed using containers is failing to launch with a 'CUDA driver version is insufficient for CUDA runtime version' error.
What is the root cause and solution?

A. The host machine's NVIDIA driver is older than the CUDA runtime version used in the container. Upgrade the host's NVIDIA driver.
B. The container is not requesting GPU resources correctly. Ensure the container runtime is configured to allow GPU access.
C. The container image has an incorrect CUDA runtime version. Rebuild the container image with a CUDA runtime version compatible with the host driver.
D. A and B
E. The 'nvidia-container-runtime' is not properly configured. Verify the configuration and restart the container runtime.

Answer: D

Explanation:
The error indicates a mismatch between host driver and container runtime versions. Upgrading the driver or rebuilding the container with a compatible runtime are the solutions.

NEW QUESTION # 52
......

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Practical NCP-AIO Information | NCP-AIO Valid Braindumps Sheet

NCP-AIO Valid Braindumps Sheet - NCP-AIO Valid Exam Answers

NVIDIA AI Operations Sample Questions (Q47-Q52):

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