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[General] Desktop and Web-based NVIDIA Practice Exams - Boost Confidence with Real NCP-AII

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【General】 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?
  • A. Both CPU pinning and NUMA awareness are critical for optimizing performance. They should be used in conjunction to achieve optimal performance.
  • B. NUMA awareness is generally more important than CPU pinning because it directly impacts memory bandwidth.
  • C. CPU pinning is generally more important than NIJMA awareness because it directly impacts CPU utilization.
  • D. CPU pinning ensures that each process/thread runs on a specific CPU core, reducing context switching overhead. NUMA awareness ensures that the CPU cores and memory used by a process are located within the same NUMA node, minimizing memory access latency.
  • E. Neither CPU pinning nor NUMA awareness are relevant for GPIJ-accelerated workloads, as the GPUs handle all the computation.
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?
  • A. All of the above
  • B. Run NCCL all-reduce benchmarks (e.g., using the NCCL tests) to measure the actual communication bandwidth between all pairs of GPUs. Compare the results to expected theoretical peak bandwidth.
  • C. Monitor GPU utilization with 'nvidia-smi' during the training run. Uneven utilization across GPUs indicates a potential communication bottleneck.
  • D. Examine the output of 'dmesg' for any NVLink-related error messages or warnings.
  • E. Use 'nvidia-smi topo -m' to visualize the GPU topology and check the reported link speeds. Any links with significantly lower speeds are suspect.
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?
  • A. PCle Gen3 x16
  • B. NVLink
  • C. Infiniband
  • D. Ethernet
  • E. PCle Gen4 x16
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?
  • A. Use a multi-stage Docker build, using a base image with the desired CUDA version for building and then copying only the necessary cuDNN libraries to a smaller runtime image.
  • B. Download the cuDNN archive from NVIDIA, extract the libraries, and manually copy them to the appropriate locations within the Dockerfile.
  • C. Use a pre-built CUDA base image and install cuDNN during the container run.
  • D. Use the NVIDIA Container Toolkit to dynamically inject the cuDNN libraries into the container at runtime.
  • E. Install the entire CUDA toolkit within the Docker image, even if only cuDNN is needed.
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?
  • A. Enable XLA (Accelerated Linear Algebra) compilation in TensorFlow.
  • B. Use 'tf.data.AUTOTIJNE to allow TensorFlow to dynamically optimize the data pipeline.
  • C. Reduce the global batch size to improve memory utilization.
  • D. Upgrade the server's network adapter to a faster interface, such as 100Gb
  • E. Increase the number of threads used for CPU-bound operations in TensorFlow using 'tf.config.threading.set_intra_op_parallelism_threads()'.
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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