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A system administrator notices that jobs are failing intermittently on Base Command Manager due to incorrect GPU configurations in Slurm. The administrator needs to ensure that jobs utilize GPUs correctly.
How should they troubleshoot this issue?
Comprehensive and Detailed Explanation From Exact Extract:
Misconfiguration related to MIG mode can cause Slurm to improperly allocate GPUs, leading to job failures. The administrator should verify whether MIG has been enabled on the GPUs and ensure that Slurm's configuration matches the hardware setup. If MIG is enabled, Slurm must be configured to recognize and schedule MIG partitions correctly to avoid resource conflicts.
A Slurm user needs to submit a batch job script for execution tomorrow.
Which command should be used to complete this task?
Comprehensive and Detailed Explanation From Exact Extract:
In Slurm cluster administration, the command to submit a batch job script is sbatch. This command schedules the job to be executed by the Slurm workload manager. The option -begin=tomorrow (or --begin=tomorrow) specifies the start time for the job execution, which in this case is set for tomorrow. The other commands have different purposes:
submit is not a valid Slurm command.
salloc is used to allocate resources interactively but does not submit batch jobs for scheduled execution.
srun runs jobs immediately on allocated resources but is typically used to launch tasks in an active job or interactively, not for batch job submission.
Therefore, the correct command to submit a batch job script for future execution is sbatch -begin=tomorrow.
Your Kubernetes cluster is running a mixture of AI training and inference workloads. You want to ensure that inference services have higher priority over training jobs during peak resource usage times.
How would you configure Kubernetes to prioritize inference workloads?
Comprehensive and Detailed Explanation From Exact Extract:
To prioritize inference workloads over training jobs in Kubernetes, administrators should configure PriorityClasses and ResourceQuotas. PriorityClasses allow assigning different priority levels to pods, ensuring that during resource contention, higher-priority pods (inference services) receive resources first. ResourceQuotas limit the resource consumption per namespace or user, controlling overall usage and reserving capacity for critical workloads. This setup effectively manages resource allocation and guarantees performance for inference jobs during peak times.
Increasing replicas or namespaces alone does not guarantee priority during contention.
HPA scales based on metrics but does not manage priority or resource guarantees directly.
A system administrator wants to run these two commands in Base Command Manager.
main
showprofile device status apc01
What command should the system administrator use from the management node system shell?
Comprehensive and Detailed Explanation From Exact Extract:
The Base Command Manager command shell (cmsh) accepts the -c flag to execute multiple commands sequentially. Using cmsh -c ''main showprofile; device status apc01'' runs the main showprofile followed by device status apc01 commands in one invocation, allowing scripted or batch execution from the management node shell.
You are tasked with deploying a DOCA service on an NVIDIA BlueField DPU in an air-gapped data center environment. The DPU has the required BlueField OS version (3.9.0 or higher) installed, and you have access to the necessary container image from NVIDIA's NGC catalog. However, you need to ensure that the deployment process is successful without an internet connection.
Which of the following steps should you take to deploy the DOCA service on the DPU?
Comprehensive and Detailed Explanation From Exact Extract:
In an air-gapped environment where the DPU has no internet connectivity, direct pulling of container images from NVIDIA's NGC catalog is not possible. The recommended approach is to manually download the required container image and YAML deployment files from a connected system, then transfer these files to the DPU. Deployment is then performed using Kubernetes with a standalone Kubelet on the DPU, which can deploy the preloaded container image offline. This ensures the deployment proceeds successfully without internet access.
66 questions covering all exam domains, starting from $20
Exam domains verified against: Official NVIDIA NCP-AIO exam guide, last checked August 2026.
Administer Fleet Command, Slurm clusters, and Base Command Manager for provisioning. Understand data center architecture for AI workloads and configure MIG for AI and HPC workloads.
Administer Kubernetes clusters and use system management tools to identify and resolve infrastructure issues. Monitor and troubleshoot workload scheduling and resource allocation.
Install and configure Base Command Manager and initialize Kubernetes on NVIDIA hosts. Deploy containers from NGC and cloud VMI containers, and understand storage requirements for AI data centers.
Sample question from this domain above: Q5
Troubleshoot Docker, NVLink and NVSwitch fabric services, Base Command Manager, and Magnum IO components. Diagnose and resolve storage performance issues affecting AI workloads.
Common questions about the exam itself
