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Title: Desktop and Web-based NVIDIA Practice Exams - Boost Confidence with Real NCP-AII [Print This Page]
Author: johnree944    Time: 16 hour before
Title: Desktop and Web-based NVIDIA Practice Exams - Boost Confidence with Real NCP-AII
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NVIDIA AI Infrastructure Sample Questions (Q195-Q200):NEW QUESTION # 195
Which of the following are key considerations when choosing between CPU pinning and NUMA (Non-Uniform Memory Access) awareness for a distributed training job on a multi-socket AMD EPYC server with multiple GPUs?
Answer: A,D
Explanation:
CPU pinning and NUMA awareness are both important for optimizing performance on multi-socket systems. CPU pinning reduces context switching overhead, while NIJMA awareness reduces memory access latency. Using them together provides the best performance. They ensure the CPU and memory being used are on the same node, which reduces latency. While GPUs handle intense computation, CPUs prepare and feed data to the GPUs, and are critical for performance.

NEW QUESTION # 196
An NVIDIA DGX server with 8 GPUs is experiencing performance issues during a distributed deep learning training run. You suspect a problem with the GPU interconnects. You have already confirmed that NVLink is active. What is the most thorough approach to diagnose potential bandwidth or latency bottlenecks in the GPU-to-GPlJ communication paths?
Answer: A
Explanation:
Explanationiagnosing GPU interconnect bottlenecks requires a multi-faceted approach. Visualizing the topology with 'nvidia-smi topo -m' , running NCCL benchmarks, monitoring GPU utilization for imbalances, and checking system logs for errors are all necessary for identifying and resolving communication issues. Ignoring any of these steps could lead to incomplete diagnosis and suboptimal performance.

NEW QUESTION # 197
You are setting up a multi-GPU AI server for deep learning. You want to ensure optimal inter-GPU communication. Which of the following interconnect technologies would provide the BEST performance?
Answer: B
Explanation:
NVLink is designed specifically for high-bandwidth, low-latency inter-GPU communication, offering significantly better performance than PCIe or network connections for workloads that benefit from it. InfiniBand is suitable for node to node communication, while NVLink is for GPU to GPU on the same node.

NEW QUESTION # 198
You are tasked with creating a custom Docker image for a deep learning application that requires a specific version of cuDNN. You want to minimize the image size while ensuring that the cuDNN libraries are correctly installed and configured. What is the most efficient way to achieve this?
Answer: A
Explanation:
A multi-stage Docker build (B) is the most efficient approach. It allows you to use a larger image with the CUDA toolkit for building and then copy only the necessary cuDNN libraries to a smaller runtime image, minimizing the final image size. Manually copying libraries (A) is tedious and error-prone. Installing the entire CUDA toolkit (C) unnecessarily increases the image size. The NVIDIA Container Toolkit (D) focuses on enabling GPU access, not dynamically injecting specific libraries. Running an install of cuDNN during the container run is problematic since the image should be self-contained.

NEW QUESTION # 199
You are tasked with optimizing an Intel Xeon scalable processor-based server running a TensorFlow model with multiple NVIDIA GPUs.
You observe that the CPU utilization is low, but the GPU utilization is also not optimal. The profiler shows significant time spent in 'tf.data' operations. Which of the following actions would MOST likely improve performance?
Answer: B
Explanation:
'tf.data' performance issues often stem from inefficient data pipelines. 'tf.data.AIJTOTUNE allows TensorFlow to dynamically optimize the pipeline by adjusting parameters such as prefetch buffer size and the number of parallel calls to transformation functions. XLA compilation optimizes graph execution, but 'tf.data' issues need to be addressed first. Increasing CPU threads might help but 'AUTOTUNE is more specific to the problem. A smaller batch size could negatively impact GPU utilization. Network upgrades are irrelevant as the problem lies within the server.

NEW QUESTION # 200
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