NCP-AIO EXAM QUESTIONS PDF | NCP-AIO VALID TEST DISCOUNT

NCP-AIO Exam Questions Pdf | NCP-AIO Valid Test Discount

NCP-AIO Exam Questions Pdf | NCP-AIO Valid Test Discount

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NVIDIA AI Operations Sample Questions (Q144-Q149):

NEW QUESTION # 144
You are configuring MIG for a Kubernetes cluster. Which of the following statements regarding the use of MIG with Kubernetes are correct? (Select TWO)

  • A. Kubernetes natively supports MIG without any additional configuration.
  • B. Kubernetes cannot schedule pods on specific MIG instances; it only schedules on the physical GPU.
  • C. The NVIDIA GPU Operator is required to enable MIG support in Kubernetes and to manage GPU resources efficiently.
  • D. MIG allows you to partition a single physical GPU into multiple virtual GPUs, enabling you to run multiple GPU-accelerated workloads in isolation within the Kubernetes cluster.
  • E. MIG is not supported in Kubernetes.

Answer: C,D

Explanation:
The NVIDIA GPU Operator is essential for managing NVIDIA GPUs, including MIG instances, within a Kubernetes cluster. MIG allows partitioning of GPUs, enabling multiple isolated workloads. Kubernetes does schedule pods on specific MIG instances with proper configuration. Native Kubernetes support isn't comprehensive without the operator. MIG is supported.


NEW QUESTION # 145
The 'nvsm' service is consistently crashing on one of your nodes. Analyzing the core dump reveals a segmentation fault related to memory access within the NVSwitch driver. What is the MOST appropriate course of action?

  • A. Increase the swap space on the node.
  • B. Recompile the NVSwitch driver with debugging symbols.
  • C. Disable the NVSwitch on the affected node.
  • D. Report the issue to NVIDIA support with the core dump and relevant system information.
  • E. Try different versions of CUDA.

Answer: D

Explanation:
Segmentation faults related to driver code usually indicate a bug within the driver itself. Reporting the issue with a core dump allows NVIDIA engineers to investigate and provide a fix. Trying to debug the driver yourself (recompiling) or disabling the NVSwitch are less effective solutions for this type of problem. Different versions of CUDA also can cause problems, but first report with the core dump.


NEW QUESTION # 146
You are tasked with designing a data center network for AI workloads that must support both RDMA over Converged Ethernet (RoCEv2) and traditional TCP/IP traffic. How should you configure the network to ensure optimal performance for both types of traffic?

  • A. Implement Priority Flow Control (PFC) and Explicit Congestion Notification (ECN) to prevent packet loss and congestion for RoCEv2 traffic.
  • B. Configure all network switches with the default settings.
  • C. Use separate physical networks for RoCEv2 and TCP/IP traffic.
  • D. Disable QOS (Quality of Service) to treat all traffic equally.
  • E. Allocate a larger MTU to TCP/IP traffic and small MTU to RoCEv2 traffic.

Answer: A

Explanation:
RoCEv2 is sensitive to packet loss and congestion. PFC and ECN are essential mechanisms to ensure reliable and high- performance RoCEv2 communication on a converged Ethernet network. Disabling QOS treats all traffic equally, which can starve RoCEv2. Using separate networks adds complexity and cost. Default settings are unlikely to be optimized for RoCEv2. PFC prevents packet loss due to congestion, and ECN provides feedback to sources to slow down before congestion occurs. Large MTUs are beneficial for both but not the primary config.


NEW QUESTION # 147
A research team wants to use a specific version of TensorFlow (e.g., TensorFlow 2.9.0) for their experiments within the Run.ai environment. What is the RECOMMENDED approach for ensuring this specific TensorFlow version is available to their jobs?

  • A. Mount a shared network drive containing TensorFlow 2.9.0 libraries into each container.
  • B. Create a custom Docker image with TensorFlow 2.9.0 pre-installed and use that image for the Run.ai jobs.
  • C. Use Run.ai's built-in environment module system to load TensorFlow 2.9.0.
  • D. Specify the TensorFlow version in the Run.ai job definition using a 'tf-version' parameter.
  • E. Install TensorFlow 2.9.0 directly on each node in the cluster.

Answer: B

Explanation:
Creating a custom Docker image with the desired TensorFlow version (2.9.0 in this case) is the recommended approach. This ensures that the job has a consistent and reproducible environment, regardless of the underlying infrastructure. Installing directly on nodes creates management overhead and potential conflicts. Run.ai does not have a built-in tf-version parameter or environment module system for this purpose. Mounting a network drive is less reliable and can introduce performance issues.


NEW QUESTION # 148
Which of the following statements is TRUE regarding the integration of GPUDirect RDMA with CUDA-Aware MPI?

  • A. CUDA-Aware MPI is only beneficial for CPU-GPU communication, while GPUDirect RDMA is used for GPU-GPU communication.
  • B. GPUDirect RDMA can be used to accelerate the underlying communication of CUDA-Aware MPI, further reducing latency and CPU overhead.
  • C. CUDA-Aware MPI is not needed when using GPUDirect Storage
  • D. CUDA-Aware MPI and GPUDirect RDMA are mutually exclusive; you can only use one at a time.
  • E. GPUDirect RDMA automatically enables CUDA-Aware MPI; no additional configuration is required.

Answer: B

Explanation:
GPUDirect RDMA optimizes the network path for communication. CUDA-Aware MPI allows MPI to directly use GPU memory without CPU copies. When used together, GPUDirect RDMA accelerates the underlying data transfers performed by CUDA-Aware MPI, leading to even greater performance improvements. CUDA-Aware MPI needs to be configured and is not automatically enabled with GPUDirect RDMA. They are designed to work together, not exclusively. CUDA-Aware MPI improves GPU-GPU communication. GPUDirect storage is for moving data, not communication between GPUs in network, so GPUDirect is still relevant.


NEW QUESTION # 149
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