Use system management tools for troubleshooting: Practice Questions — Workload Management (NVIDIA-Certified Professional: AI Operations)

Practice Questions: Using System Management Tools for Troubleshooting in NVIDIA AI Operations

This set of multiple-choice questions is designed to help candidates prepare for the Use system management tools for troubleshooting portion of the Workload Management topic in the NVIDIA-Certified Professional: AI Operations exam. Each question tests your understanding of key troubleshooting techniques using system management tools in NVIDIA AI infrastructure.

1. Which NVIDIA tool provides detailed GPU utilization and temperature metrics useful for diagnosing performance bottlenecks?

   • A. nvidia-smi
   • B. Run:ai CLI
   • C. Slurm Scheduler
   • D. Kubernetes Dashboard

   Correct answer: A

   Explanation: nvidia-smi is the primary NVIDIA system management tool that reports real-time GPU metrics such as utilization, temperature, and memory usage, essential for troubleshooting GPU-related issues.

2. When a containerized AI inference workload deployed from NGC is failing to start, which command helps inspect container logs for error messages?

   • A. kubectl logs <pod-name>
   • B. scontrol show job <job-id>
   • C. runai describe <job-name>
   • D. nvidia-smi topo --matrix

   Correct answer: A

   Explanation: kubectl logs retrieves logs from Kubernetes pods, which is critical for diagnosing container startup failures in inference workloads deployed on Kubernetes.

3. Which Slurm command provides detailed information about the status and resource usage of a training job?

   • A. squeue
   • B. scontrol show job <job-id>
   • C. sacct
   • D. sinfo

   Correct answer: B

   Explanation: scontrol show job displays comprehensive job details including allocated nodes, resource usage, and job state, making it invaluable for troubleshooting training workloads managed by Slurm.

4. Which system management tool would you use to monitor GPU resource allocation across multiple teams sharing a cluster?

   • A. Run:ai dashboard
   • B. nvidia-smi
   • C. kubectl get pods
   • D. slurmd daemon logs

   Correct answer: A

   Explanation: The Run:ai dashboard provides centralized visibility into GPU resource allocation and usage across teams, facilitating troubleshooting of resource contention issues.

5. What is the primary purpose of the nvidia-debugdump tool in troubleshooting AI workloads?

   • A. To collect detailed GPU hardware and driver state for post-mortem analysis
   • B. To schedule AI training jobs on GPUs
   • C. To deploy containers from NGC
   • D. To monitor Kubernetes pod status

   Correct answer: A

   Explanation: nvidia-debugdump collects low-level GPU state and driver information useful for deep troubleshooting and diagnosing hardware or driver faults.

6. Which Kubernetes command helps identify resource constraints causing pod evictions in AI inference workloads?

   • A. kubectl describe pod <pod-name>
   • B. runai submit
   • C. scontrol hold <job-id>
   • D. nvidia-smi -q

   Correct answer: A

   Explanation: kubectl describe pod shows events and status details, including reasons for pod evictions such as memory or CPU limits, aiding troubleshooting of inference workload failures.

7. When troubleshooting GPU memory leaks in training workloads managed by Slurm, which log file is most helpful?

   • A. Slurm job output and error logs
   • B. Kubernetes pod logs
   • C. Run:ai usage reports
   • D. nvidia-smi topo output

   Correct answer: A

   Explanation: Slurm job output and error logs capture runtime messages from training jobs, which can reveal memory leak symptoms or errors during execution.

8. Which Run:ai CLI command can you use to check the real-time status and resource usage of a submitted AI workload?

   • A. runai list
   • B. runai submit
   • C. runai logs
   • D. runai describe

   Correct answer: D

   Explanation: runai describe provides detailed information about a workload’s current status, resource allocation, and usage, which is essential for troubleshooting and monitoring.

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