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NCP-AII Valid Test Voucher & NCP-AII New Dumps

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NCP-AII Valid Test Voucher & NCP-AII New Dumps

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NVIDIA AI Infrastructure Sample Questions (Q205-Q210):NEW QUESTION # 205
An AI inference workload running on a server equipped with NVIDIA TensorRT is experiencing intermittent performance degradation. Profiling reveals potential issues with GPU utilization. Which command-line tool is best suited to provide real-time GPU performance metrics, including utilization, memory usage, and power consumption, during workload execution?
  • A. gpustat
  • B. tegrastats
  • C. nvitop
  • D. nvidia-smi
  • E. nvprof
Answer: D
Explanation:
'nvidia-smi' (NVIDIA System Management Interface) is the primary tool for monitoring GPU performance in real-time, including utilization, memory usage, and power consumption. While 'nvprof is a profiler, it's more suited for in-depth analysis after the run. 'nvitop' and gpustat are third-party tools that provide similar functionalities as -nvidia-smr. 'tegrastats' is specific to NVIDIA Jetson devices.

NEW QUESTION # 206
Which of the following statements regarding the benefits of using a BlueField DPU for network offload are TRUE? (Select TWO)
  • A. Elimination of the need for a dedicated network interface card (NIC).
  • B. Reduced CPU utilization on the host server for network-related tasks.
  • C. Automatic compatibility with all existing network protocols without requiring software updates.
  • D. Increased network throughput due to hardware acceleration.
  • E. Simplified network configuration compared to traditional NICs.
Answer: B,D
Explanation:
BlueField DPUs offload network tasks, freeing up CPU resources and improving network performance through hardware acceleration. Simplified configuration is not always guaranteed, as DPUs often require more configuration due to their advanced features. DPUs do not eliminate the need for a NIC, they are a NIC with advanced processing capabilities.

NEW QUESTION # 207
You are troubleshooting a performance issue with a GPU-accelerated application running inside a Docker container. The 'nvidia-smi' output inside the container shows the GPU is being utilized, but the performance is significantly lower than expected. Which of the following could be the cause of this performance bottleneck?
  • A. The GPU is overheating, causing thermal throttling.
  • B. The application is performing frequent small memory transfers between the CPU and GPIJ.
  • C. The host machine's CPU is being heavily utilized, causing a bottleneck in data transfer to the GPU.
  • D. The version of the CUDA driver on the host is incompatible with the CUDA toolkit version used in the container.
  • E. The Docker container is not configured to use shared memory for data transfer with the GPU.
Answer: A,B,C,D
Explanation:
Several factors could contribute to reduced GPU performance within a Docker container, even if the GPU is being utilized. A heavily loaded CPU (A) can bottleneck data transfer to the GPU. Incompatible CUDA driver versions between host and container (C) cause unexpected errors, and CUDA drivers are important for GPU support. Frequent small memory transfers between CPU and GPU (D) can be inefficient. Overheating (E) can cause the GPU to throttle its performance. While shared memory optimization (B) can help, it's not always the primary cause of the initial performance drop.

NEW QUESTION # 208
You are monitoring a server with 8 GPUs used for deep learning training. You observe that one of the GPUs reports a significantly lower utilization rate compared to the others, even though the workload is designed to distribute evenly. 'nvidia-smi' reports a persistent "XID 13" error for that GPU. What is the most likely cause?
  • A. A driver bug causing incorrect workload distribution.
  • B. The GPU's compute mode is set to 'Exclusive Process'.
  • C. Insufficient system memory preventing data transfer to that GPU.
  • D. A hardware fault within the GPU, such as a memory error or core failure.
  • E. An incorrect CUDA version installed.
Answer: D
Explanation:
XID 13 errors in 'nvidia-smi' typically indicate a hardware fault within the GPU. Driver bugs or memory issues would likely cause different error codes or system instability across multiple GPUs. CUDA version mismatch might prevent the application from running altogether, but is less likely to lead to a specific XID error on a single GPU. Exclusive Process mode will lead to it being used by a different process but not necessarily cause that XID error.

NEW QUESTION # 209
Consider a scenario where you need to isolate GPU workloads in a multi-tenant Kubernetes cluster. Which of the following Kubernetes constructs would be MOST suitable for achieving strong isolation at both the resource and network level?
  • A. Using pod affinity and anti-affinity rules to control pod placement.
  • B. Using labels and selectors to schedule workloads on specific GPU nodes.
  • C. Using namespaces with resource quotas and network policies.
  • D. Using taints and tolerations to dedicate GPU nodes to specific workloads.
  • E. Using node affinity only.
Answer: C
Explanation:
Namespaces provide logical isolation within a Kubernetes cluster. Resource quotas limit the resources (including GPIJs) that a namespace can consume, while network policies control network traffic between namespaces, ensuring strong isolation. Options B, C, D, and E provide some level of control over pod placement but do not offer the same level of resource and network isolation as namespaces with resource quotas and network policies.

NEW QUESTION # 210
......
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