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[General] Are NVIDIA NCP-AIO Actual Questions Effective to Get Certified?

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【General】 Are NVIDIA NCP-AIO Actual Questions Effective to Get Certified?

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NVIDIA AI Operations Sample Questions (Q52-Q57):NEW QUESTION # 52
You are managing a high availability (HA) cluster that hosts mission-critical applications. One of the nodes in the cluster has failed, but the application remains available to users.
What mechanism is responsible for ensuring that the workload continues to run without interruption?
  • A. Load balancing across all nodes in the cluster.
  • B. Data replication between nodes to ensure data integrity.
  • C. Manual intervention by the system administrator to restart services.
  • D. The failover mechanism that automatically transfers workloads to a standby node.
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In an HA cluster, thefailover mechanismis responsible for detecting node failures and automatically transferring workloads to a standby or redundant node to maintain service availability. This process ensures mission-critical applications continue running without interruption. Load balancing helps distribute traffic but does not handle node failures. Manual intervention is not ideal for HA, and data replication ensures data integrity but does not itself manage workload continuity.

NEW QUESTION # 53
How can you ensure that all newly provisioned nodes in your BCM cluster automatically have the necessary NVIDIA drivers and container runtime installed?
  • A. Rely on the NVIDIA automatic driver installation tool after the OS is booted.
  • B. Manually install the drivers and runtime on each node after provisioning.
  • C. Create a custom OS image with the drivers and runtime pre-installed and use that image for provisioning.
  • D. Use a Kubernetes DaemonSet to install the drivers and runtime on each node after it joins the cluster.
  • E. Configure BCM to run a post-provisioning script that installs the drivers and runtime.
Answer: C,D
Explanation:
A custom OS image ensures drivers and runtime are present from the start. A post-provisioning script allows automated installation. Manual installation is not scalable. A DaemonSet installs software after the node joins the cluster, but BCM configuration happens at provisioning. The NVIDIA automatic driver installation tool might not be compatible with all BCM configurations.

NEW QUESTION # 54
A user complains that their AI training job is running very slowly. Upon investigation, you discover that the pod is scheduled onto a node with a slow network connection, causing significant delays in data transfer. How would you ensure that future similar jobs are scheduled onto nodes with faster network connections?
  • A. Manually reschedule the pod onto a node with a faster network.
  • B. Implement node affinity rules based on network bandwidth labels, and label the nodes appropriately.
  • C. Use inter-pod affinity to force the job onto nodes already running network-intensive workloads.
  • D. Configure the kubelet to prioritize pods based on their network usage.
  • E. Increase the resource requests for the pod to trigger rescheduling.
Answer: B
Explanation:
The correct answer is B. By labeling nodes with their network bandwidth capabilities (e.g., 'network-bandwidth: 100GbpS), you can then use node affinity rules in your pod specifications to ensure that jobs requiring high bandwidth are scheduled onto suitable nodes. Option A is a temporary fix. Options C and D do not address the core issue of network bandwidth. Option E would exacerbate the problem by concentrating network-intensive workloads on the same nodes.

NEW QUESTION # 55
You are troubleshooting a distributed deep learning training job that utilizes GPUDirect Storage for data loading and CUDA-Aware MPI with GPUDirect RDMA for inter-GPU communication. The training process is significantly slower than expected, and you suspect a bottleneck in the data pipeline. You've used 'nvprof and determined that the data loading phase is taking an unusually long time. Which of the following steps would be the MOST effective next step in diagnosing the issue? SELECT TWO.
  • A. Check the PCIe bandwidth utilization between the storage devices and the GPUs.
  • B. Verify that the storage devices being used support GPUDirect Storage.
  • C. Monitor the CPU utilization during the data loading phase.
  • D. Profile the network bandwidth
  • E. Examine the NCCL logs for communication errors.
Answer: A,B
Explanation:
Explanation: Given that 'nvprof indicates a slow data loading phase, the most effective next steps are to: 1. Verify that the storage devices support GPUDirect Storage: If the storage devices do not properly support GPUDirect Storage, the data will likely be transferred through the CPU, negating the performance benefits. 2. Check the PCIe bandwidth utilization: Even if the storage devices support GPUDirect Storage, the PCIe link between the storage devices and the GPUs may be saturated, limiting the data transfer rate. High CPU utilization (A) might indicate that GPUDirect Storage is not working correctly, but verifying storage support is more direct. NCCL logs (D) are more relevant for inter-GPU communication issues. Network bandwidth (E) impacts inter-GPU communication, but the problem is data loading.

NEW QUESTION # 56
You are deploying a containerized application from NGC that relies on the NVIDIA Data Loading Library (DALI) for efficient data preprocessing. You want to ensure that DALI can access the GPU within the container. What steps are necessary to configure DALI correctly?
  • A. Use DALI's function to specify the GPU device ID within the DALI pipeline.
  • B. Set the environment variable to specify the GPU to be used by DALI.
  • C. Ensure that the NVIDIA Container Toolkit is installed and configured on the host system.
  • D. Configure DALI to use the CPU for data preprocessing instead of the GPU.
  • E. Install the NVIDIA drivers directly within the container image.
Answer: A,B,C
Explanation:
The NVIDIA Container Toolkit enables GPU access. 'CUDA_VISIBLE DEVICES' controls GPU visibility. specifies the GPU device within the DALI pipeline. A is incorrect; drivers are provided by the host. D defeats the purpose of using DALI for GPU-accelerated data preprocessing.

NEW QUESTION # 57
......
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