NCP-AIO NVIDIA AI OPERATIONS DUMPS FOR ULTIMATE RESULTS 2025

NCP-AIO NVIDIA AI Operations Dumps For Ultimate Results 2025

NCP-AIO NVIDIA AI Operations Dumps For Ultimate Results 2025

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NVIDIA AI Operations Sample Questions (Q58-Q63):

NEW QUESTION # 58
You are responsible for deploying a deep learning model for real-time inference using Triton Inference Server from NGC. Latency is a critical requirement. Which of the following optimization techniques can you employ to minimize inference latency?

  • A. Reduce the model's precision (e.g., from FP32 to FP16 or INT8).
  • B. Disable CUDA graphs to improve CPU utilization.
  • C. Use dynamic batching to aggregate multiple inference requests into a single batch.
  • D. Optimize the model's architecture and code for GPU execution.
  • E. Increase the number of instances of the model deployed on the Triton server.

Answer: A,C,D,E

Explanation:
A, B, C and E are correct. Increasing instances allows for parallel processing. Dynamic batching improves throughput. Reducing precision accelerates computation. Model optimization enhances GPU utilization. D is generally incorrect; CUDA graphs typically improve performance by reducing kernel launch overhead.


NEW QUESTION # 59
Which BCM configuration file defines the operating system image used for provisioning new nodes?

  • A. cluster.yaml
  • B. image.yaml
  • C. node.yaml
  • D. bcm.conf
  • E. provisioning.yaml

Answer: A

Explanation:
The 'cluster.yamr file in BCM typically contains the definition of the operating system image, including the URL or path to the image file, along with other cluster-level configuration details like network settings and Kubernetes version.


NEW QUESTION # 60
You have a Docker container running a CUDA application. You notice that the container takes a long time to start, specifically when initializing the CUDA context. How can you troubleshoot and potentially improve the startup time?

  • A. Reduce the number of CUDA devices visible to the container using the environment variable to only expose necessary GPUs.
  • B. Pre-initialize CUDA in the background. Launch a background process to initialize the CUDA context before the main application starts.
  • C. Use the NVIDIA CUDA Cache. Set the environment variable to a persistent volume to cache compiled CUDA kernels across container restarts.
  • D. Use lazy loading techniques in your application to delay the initialization of CUDA-dependent modules until they are actually needed.
  • E. Use a lighter base image. A smaller image will generally have a quicker startup time.

Answer: A,B,C,D

Explanation:
CUDA context creation is time-consuming. CUDA cache (A) speeds up subsequent startups. Limiting visible devices (B) reduces the initialization overhead. Pre-initializing CUDA (D) amortizes the cost. Lazy loading (E) avoids unnecessary initializations. Using a lighter base image may help, but not as directly as the other options.


NEW QUESTION # 61
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. Ensure that the NVIDIA Container Toolkit is installed and configured on the host system.
  • B. Set the environment variable to specify the GPU to be used by DALI.
  • C. Use DALI's function to specify the GPU device ID within the DALI pipeline.
  • D. Install the NVIDIA drivers directly within the container image.
  • E. Configure DALI to use the CPU for data preprocessing instead of the GPU.

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 # 62
You're managing a cluster that uses Kubernetes and the NVIDIA Device Plugin. A pod requests a GPU using resource limits. The pod starts, but the application within the pod reports that no GPUs are available. What troubleshooting steps should you take FIRST?

  • A. Inspect the Kubernetes node's status using 'kubectl describe node to ensure that the node is reporting GPU resources as available.
  • B. Verify that the NVIDIA Device Plugin is correctly installed and running on the Kubernetes nodes using 'kubectl describe daemonset nvidia-device-plugin-daemonset -n kube-system'.
  • C. Check the pod's YAML definition to ensure that the GPU resource request is correctly specified (e.g., 'nvidia.com/gpu: 1
  • D. Restart the Kubernetes kubelet service on the node.
  • E. Reinstall the NVIDIA drivers on the host.

Answer: A,B,C

Explanation:
The initial steps should focus on verifying the correct setup of the NVIDIA Device Plugin (A), ensuring the pod correctly requests GPU resources (B), and confirming that Kubernetes recognizes the GPU resources on the node (C). Restarting the kubelet (D) or reinstalling drivers (E) are more drastic measures that should be considered after confirming the basic configuration.


NEW QUESTION # 63
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