100% PASS NVIDIA - NCP-AIO - TRUSTABLE NVIDIA AI OPERATIONS QUIZ

100% Pass NVIDIA - NCP-AIO - Trustable NVIDIA AI Operations Quiz

100% Pass NVIDIA - NCP-AIO - Trustable NVIDIA AI Operations Quiz

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NVIDIA AI Operations Sample Questions (Q51-Q56):

NEW QUESTION # 51
An AI company is planning to expand its AI infrastructure to support larger and more complex models. They currently use a storage solution based on HDDs connected directly to the compute servers. The AI engineers have complained that the performance is a bottleneck for training. The CTO suggest to use a disaggregated storage model with NVMe-oF connecting a shared storage system. What are the main aspects of this approach that need to be carefully considered before its implementation?

  • A. The network latency of the NVMe-oF network must be minimized to maintain low access times to the data.
  • B. The total available RAM on each server.
  • C. The NVMe-oF network infrastructure bandwidth must be sufficient to support the aggregate 1/0 demands of all compute servers concurrently.
  • D. The storage system should provide enough IOPS and bandwidth to prevent it from becoming a bottleneck in itself.
  • E. The increase of the number of HDDs connected to compute servers and configuring a local RAID configuration.

Answer: A,C,D

Explanation:
With a disaggregated model, A, B, and C are the most important considerations. If the NVMe-oF is not sized correctly and do not have sufficient performance, the centralized storage becomes the bottleneck, invalidating the purpose of the exercise. Option D is not valid, as this is the current state of the infrastructure. Option E is irrelevant in the decision process.


NEW QUESTION # 52
Which BCM configuration file defines the operating system image used for provisioning new nodes?

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

Answer: B

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 # 53
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. Use the NVIDIA CUDA Cache. Set the environment variable to a persistent volume to cache compiled CUDA kernels across container restarts.
  • C. Pre-initialize CUDA in the background. Launch a background process to initialize the CUDA context before the main application starts.
  • D. Use a lighter base image. A smaller image will generally have a quicker startup time.
  • E. Use lazy loading techniques in your application to delay the initialization of CUDA-dependent modules until they are actually needed.

Answer: A,B,C,E

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 # 54
A fleet of edge devices running AI inference applications experiences intermittent network connectivity. You need to configure Fleet Command to handle these disruptions gracefully. Which of the following actions should you take to ensure application resilience?

  • A. Configure Fleet Command to immediately roll back deployments when network connectivity is lost.
  • B. Increase the timeout values for all Fleet Command operations.
  • C. Instruct users to manually restart applications on the edge devices after network outages.
  • D. Disable all updates to the edge devices during periods of network instability.
  • E. Implement a local caching mechanism on the edge devices to store inference results during network outages and synchronize them when connectivity is restored.

Answer: E

Explanation:
A local caching mechanism allows edge devices to continue operating during network disruptions, ensuring application resilience. Rolling back deployments (A) is disruptive. Disabling updates (C) prevents improvements. Increasing timeouts (D) might help with transient issues but doesn't address the underlying problem. Manual restarts (E) are not scalable or reliable.


NEW QUESTION # 55
You need to upgrade the Run.ai platform on your on-premise Kubernetes cluster. What is the RECOMMENDED procedure for performing this upgrade to minimize downtime and ensure a smooth transition?

  • A. Upgrade the Kubernetes cluster itself before upgrading Run.ai.
  • B. Delete the existing Run.ai deployment and install the new version from scratch.
  • C. Simply restart all Run.ai pods in the cluster.
  • D. Follow the official Run.ai documentation for the specific upgrade path, typically involving a rolling update procedure using Helm or kubectl apply.
  • E. Edit the Run.ai deployment YAML file directly and apply the changes.

Answer: D

Explanation:
Following the official Run.ai documentation is crucial for a smooth upgrade. Run.ai typically provides detailed instructions for upgrading the platform, often involving a rolling update procedure using Helm or kubectl apply. This minimizes downtime by updating components incrementally. Deleting and reinstalling is disruptive and risky. Directly editing the deployment YAML is not recommended. While Kubernetes version compatibility is important, Run.ai upgrades should generally be performed independently, following Run.ai's documented procedure. Restarting pods is insufficient for a platform upgrade.


NEW QUESTION # 56
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