Slurm for AI Training Clusters Quiz

Answers and explanations appear as you take the quiz.

1. What command do users typically use to submit a batch job to Slurm?
   • sbatch
   • kubectl apply
   • docker run
   • git push
2. How does a Slurm job request GPUs?
   • By editing DNS records
   • Through a --gres=gpu directive
   • Via a web form only
   • GPUs cannot be requested in Slurm
3. Which Slurm component makes the central scheduling decisions?
   • slurmd
   • srun
   • slurmctld
   • sbatch
4. Why can srun help launch distributed training tasks inside a Slurm allocation?
   • It automatically restores every model checkpoint
   • It replaces slurmctld as the scheduler
   • It converts a single GPU into multiple physical GPUs
   • It launches coordinated tasks across allocated nodes; the training setup may still need explicit DDP configuration
5. What is the purpose of backfill scheduling in Slurm?
   • To run short jobs in idle gaps without delaying higher-priority work
   • To delete old jobs
   • To overclock GPUs
   • To encrypt job logs
6. What must an AI training script do so a requeued Slurm job can continue after a node failure?
   • Assume Slurm stores model weights automatically
   • Save checkpoints and reload them when the job restarts
   • Disable resource accounting
   • Submit a new job without restoring state
7. Which Slurm daemon normally runs on each compute node to launch tasks and report node status?
   • slurmctld
   • sbatchd
   • slurmd
   • gresctl
8. What can Slurm resource accounting help cluster administrators track?
   • The model’s validation accuracy automatically
   • The semantic content of training data
   • Whether every checkpoint is scientifically valid
   • Job resource use such as allocated CPUs, GPUs and time