최신 NVIDIA-Certified Professional NCP-AIO 무료샘플문제:

1. What is the main purpose of using Multi-lnstance GPU (MIG) with NVIDIA GPUs in a Kubernetes cluster managed by BCM, and what challenges does it help to address?

A) To simplify the deployment and management of GPU drivers and NVIDIA Container Toolkit on Kubernetes nodes.
B) To enable the execution of CUDA-unaware applications within Kubernetes by abstracting away the underlying GPU hardware.
C) To improve the overall performance of GPU-accelerated applications by allowing multiple containers to share a single GPU's memory and compute resources concurrently.
D) To increase GPU utilization and enable resource sharing in multi-tenant environments by partitioning a physical GPU into multiple isolated instances.
E) To facilitate running CPU-bound workloads directly on GPUs to maximize resource utilization.

2. You've deployed a container from NGC containing a computationally intensive AI model training script. You notice that the container is consistently being killed by the Kubernetes OOMKiller, even though the node has sufficient memory available. What are the possible causes and solutions?

A) The node is running out of swap space, causing the OOMKiller to terminate processes aggressively.
B) The container's memory limit is set too low, causing it to exceed its allocated memory.
C) The application within the container has a memory leak, leading to excessive memory consumption.
D) Profile the application's memory usage to identify and fix memory leaks.
E) Increase the container's memory limit in the Kubernetes deployment manifest.

3. You have a Docker container running a TensorFlow model for image classification. The container is performing well initially, but after a few hours, the inference speed drops significantly. How do you troubleshoot this performance degradation?

A) Check the Docker container logs for any error messages or warnings that might indicate a problem.
B) Monitor CPU and GPU utilization inside the container using tools like 'top', 'htop' , and 'nvidia-smi' to identify resource bottlenecks.
C) Check network connectivity between the container and any external services it relies on.
D) Restart the Docker container to clear any accumulated memory or resource leaks.
E) Profile the TensorFlow model using TensorFlow's profiling tools to identify performance bottlenecks in the model's execution.

4. You need to configure network settings for your Fleet Command deployment. You want to ensure that edge devices can only communicate with the Fleet Command server over a specific port and protocol for security reasons. Which of the following configurations is the MOST appropriate?

A) Disable all network access on the edge devices except for SSH.
B) Rely on the default network settings provided by the operating system.
C) Configure a VPN for all communication, even local communication.
D) Configure a firewall on the edge devices and the Fleet Command server to allow communication only on the designated port and protocol (e.g., HTTPS on port 443),
E) Open all ports on the edge devices and the Fleet Command server to allow unrestricted communication.

5. You are managing a cluster with multiple nodes connected via NVLink and NVSwitch. After a network outage, some of the NVLink connections are showing as 'degraded' in 'nvsm show links'. What steps should you take to attempt to restore the connections to their optimal state? (Select TWO correct answers)

A) Check physical NVLink cable connections for damage or looseness.
B) Reboot all nodes in the cluster simultaneously.
C) Run 'nvsm repair linkS on the affected nodes.
D) Restart the 'nvsm' service on all nodes.
E) Update the BIOS on all servers.

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