Troubleshoot Docker, fabric manager, and Base Command Manager: Practice Questions — Troubleshooting and Optimization (NVIDIA-Certified Professional: AI Operations)

Practice Questions: Troubleshoot Docker, Fabric Manager, and Base Command Manager These multiple-choice questions are designed to help you prepare...

Practice Questions: Troubleshoot Docker, Fabric Manager, and Base Command Manager

These multiple-choice questions are designed to help you prepare for the Troubleshooting and Optimization section of the NVIDIA-Certified Professional: AI Operations exam, specifically focusing on troubleshooting Docker, Fabric Manager, and Base Command Manager components.

  1. Which command would you use to check the status of Docker containers on a node?

    • A) docker ps -a
    • B) docker images
    • C) docker logs
    • D) docker info

    Correct answer: A

    Explanation: docker ps -a lists all containers, including running and stopped, providing their status. This is essential for troubleshooting container states.

  2. When Fabric Manager is not detecting GPUs correctly, which log file is most useful for initial diagnosis?

    • A) /var/log/fabric_manager.log
    • B) /var/log/syslog
    • C) /var/log/docker.log
    • D) /var/log/nvidia-smi.log

    Correct answer: A

    Explanation: Fabric Manager maintains its own log file at /var/log/fabric_manager.log, which contains detailed information about GPU detection and fabric status.

  3. Which Base Command Manager feature allows you to monitor real-time GPU utilization across a cluster?

    • A) Job submission dashboard
    • B) GPU telemetry dashboard
    • C) Container registry browser
    • D) User access control panel

    Correct answer: B

    Explanation: The GPU telemetry dashboard in Base Command Manager provides real-time monitoring of GPU utilization, which is critical for troubleshooting performance issues.

  4. What is the most common cause of Docker container startup failures related to NVIDIA GPU access?

    • A) Incorrect Docker image tag
    • B) Missing NVIDIA Container Toolkit installation
    • C) Insufficient CPU resources
    • D) Network connectivity issues

    Correct answer: B

    Explanation: Without the NVIDIA Container Toolkit installed and properly configured, Docker containers cannot access GPUs, causing startup failures.

  5. Which command helps verify if Fabric Manager is running and managing the GPUs correctly?

    • A) systemctl status nvidia-fabricmanager
    • B) nvidia-smi -L
    • C) docker ps
    • D) basecommand status

    Correct answer: A

    Explanation: systemctl status nvidia-fabricmanager checks the Fabric Manager service status, confirming if it is active and managing GPUs.

  6. In Base Command Manager, if a user reports that their container failed to launch, which of the following is the best first step?

    • A) Restart the Base Command Manager service
    • B) Review the container deployment logs in the Base Command Manager UI
    • C) Rebuild the Docker image
    • D) Reinstall Fabric Manager

    Correct answer: B

    Explanation: Reviewing deployment logs provides direct insight into the failure cause, making it the best initial troubleshooting step.

  7. Which Docker command can be used to retrieve logs from a specific container to diagnose issues?

    • A) docker inspect CONTAINER_ID
    • B) docker logs CONTAINER_ID
    • C) docker exec CONTAINER_ID
    • D) docker stats CONTAINER_ID

    Correct answer: B

    Explanation: docker logs fetches the standard output and error logs from a container, which is essential for diagnosing runtime problems.

  8. What is a common troubleshooting step if Fabric Manager reports GPU fabric errors?

    • A) Update the Docker image
    • B) Restart the Fabric Manager service
    • C) Increase container CPU limits
    • D) Disable Base Command Manager

    Correct answer: B

    Explanation: Restarting the Fabric Manager service can resolve transient errors related to GPU fabric communication.

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Related topics:

#NVIDIA #AIOperations #DockerTroubleshooting #FabricManager #BaseCommandManager

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