OTHER NVIDIA NCP-AIO EXAM KEYWORDS

Other NVIDIA NCP-AIO Exam Keywords

Other NVIDIA NCP-AIO Exam Keywords

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NVIDIA AI Operations Sample Questions (Q44-Q49):

NEW QUESTION # 44
You've deployed a container from NGC on a Kubernetes cluster, but the application is experiencing intermittent GPU errors. You suspect memory leaks within the container are causing the issue. What is the most effective method to diagnose this problem?

  • A. Restart the container regularly to clear potential memory leaks.
  • B. Use NVIDIA Nsight Systems to profile the application and identify memory allocation patterns.
  • C. Monitor the container's CPU usage using 'kubectl top pod'.
  • D. Use 'nvidia-smi' within the container to monitor GPU memory usage over time.
  • E. Analyze the application's logs for CUDA error messages related to memory allocation.

Answer: B,D,E

Explanation:
B, C, and E are correct. 'nvidia-smi' provides real-time GPU memory usage. Application logs often contain CUDA errors indicating memory issues. Nsight Systems offers detailed profiling to pinpoint memory leaks. A is not relevant to GPU memory leaks. D is a workaround, not a diagnostic solution.


NEW QUESTION # 45
You need to configure node health checks in Slurm to automatically detect and drain unhealthy nodes. Which of the following approaches is the MOST robust and recommended way to achieve this?

  • A. Relying solely on the 'SuspendExcNodeList' parameter to prevent jobs from being scheduled on potentially problematic nodes.
  • B. Writing a custom script and adding it to the 'HealthCheckProgram' parameter in 'slurm.conf.
  • C. Disabling node heartbeat messages to avoid false positives.
  • D. Using the 'scontrol update node' command to manually mark nodes as DOWN when issues are detected.
  • E. Creating a cron job that periodically checks node health using 'sinfo' and marks failing nodes as DOWN.

Answer: B

Explanation:
The ' HealthCheckProgram' parameter in 'slurm.conf allows you to specify a script that Slurm will periodically execute to check the health of each node. This provides an automated and robust mechanism for detecting and draining unhealthy nodes. Manually updating node states is not scalable, and relying solely on 'SuspendExcNodeList' is not proactive.


NEW QUESTION # 46
After completing the installation of a Kubernetes cluster on your NVIDIA DGX systems using BCM, how can you verify that all worker nodes are properly registered and ready?

  • A. Check each node manually by logging in via SSH and verifying system status with systemctl.
  • B. Run kubectl get nodes to verify that all worker nodes show a status of "Ready".
  • C. Run kubectl get pods to check if all worker pods are running as expected.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The standard method to verify that worker nodes are correctly registered and ready in a Kubernetes cluster is to runkubectl get nodes. This command lists all nodes and their statuses. Nodes showing a status of"Ready" indicates they are properly connected and available to schedule workloads. Checking pods or manual SSH is not the direct or reliable way to verify node readiness.


NEW QUESTION # 47
You're using Docker Compose to manage a multi-container application that includes a GPU-accelerated container. The application runs fine locally, but when deployed to a cloud environment, the GPU container fails to start with a 'device not found' error. What are the potential reasons for this failure?

  • A. The NVIDIA drivers are not installed on the cloud instance. Install the appropriate NVIDIA drivers for the cloud instance's operating system.
  • B. The cloud environment does not have NVIDIA GPUs available. Verify that the cloud instance type includes NVIDIA GPUs.
  • C. The Docker Compose file does not specify the '-gpus all' flag for the GPU container. Add 'deploy: ' and 'resources:' sections to your docker-compose.yml to specify GPU requirements.
  • D. The Docker image is too large to be deployed in the cloud environment. Optimize the Docker image size to reduce deployment time.
  • E. The Docker daemon on the cloud instance is not configured to use the NVIDIA runtime. Configure the Docker daemon as described in NVIDIA's documentation.

Answer: A,B,C,E

Explanation:
All options except E are potential reasons for failure. The cloud environment might lack GPUs, the necessary drivers might be missing, the Docker daemon might be misconfigured, or the Docker Compose file might not explicitly request GPU resources. Option E is usually not the cause, but optimizing image size is always a good practice.


NEW QUESTION # 48
Which of the following statements regarding the NVIDIA Device Plugin for Kubernetes are correct?

  • A. It ensures that containers have the necessary NVIDIA libraries and tools.
  • B. It replaces the need for the NVIDIA Container Toolkit.
  • C. It exposes GPUs as schedulable resources to Kubernetes.
  • D. It allows Kubernetes to be aware of the NVIDIA GPUs present on the nodes.
  • E. It automatically installs the NVIDIA drivers on the nodes.

Answer: C,D

Explanation:
The correct answers are A and C. The NVIDIA Device Plugin discovers NVIDIA GPUs on each node and advertises them as resources to the Kubernetes scheduler. It enables Kubernetes to allocate GPUs to containers. It does not install drivers (that's a separate process). It works with the NVIDIA Container Toolkit to provide the necessary libraries within the container. It does not replace the NVIDIA Container Toolkit; they work in conjunction.


NEW QUESTION # 49
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