NVIDIA NVIDIA AI Operations NCP-AIO Exam Dumps: Updated Questions & Answers (October 2025)

Comprehensive and Detailed Explanation From Exact Extract:

When automating tasks with the Run:AI Administrator CLI, it is essential to ensure that theKubernetes configuration file (kubeconfig)is correctly set up with cluster administrative rights. This enables the CLI to interact programmatically with the Kubernetes API for managing nodes, resources, and workloads efficiently. Without proper administrative permissions in the kubeconfig, automated operations will fail due to insufficient rights.

Manual GPU allocation is typically handled by scheduling policies rather than CLI manual assignments. The CLI does not replacekubectlcommands entirely, and installation on Windows is not a critical requirement.

The Run:AI Administrator CLI requires a Kubernetes configuration file with cluster-administrative rights in order to perform automation or scripting tasks across the cluster. Without those rights, the CLI cannot manage nodes or resources programmatically.

Comprehensive and Detailed Explanation From Exact Extract:

NVIDIA Fabric Manager is designed to manage and optimize fabric resources like NVLink and NVSwitch inenterprise-class platforms such as HGX and DGX systems. These platforms have the necessary hardware fabric components. The L40S Certified and GeForce series are either not compatible or do not require Fabric Manager.

=============

Comprehensive and Detailed Explanation From Exact Extract:

Misconfiguration related toMIG modecan 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.

=============

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.

Comprehensive and Detailed Explanation From Exact Extract:

When troubleshooting performance bottlenecks related to feeding data consistently to GPUs during deep learning workloads, the key storage metric to consider is sequential read speed. Deep learning training typically involves streaming large datasets sequentially from storage to GPUs. The sequential read speed measures how fast data can be read in a continuous stream, directly impacting the ability to keep GPUs fed without stalls.

Disk I/O operations per second (IOPS) measures random read/write operations and is less relevant for large sequential data streams in AI workloads.

Disk free space indicates available storage capacity but does not impact data feed rate.

Disk utilization in performance manager shows overall usage but does not specify the speed or consistency of data feed.

Therefore, focusing on sequential read speed (option C) is critical for ensuring consistent, high-throughput data feeding to GPUs, minimizing bottlenecks in deep learning runtime environments.

This is consistent with NVIDIA AI Operations best practices for system performance optimization and troubleshooting storage-related issues in AI infrastructure.

=============

Comprehensive and Detailed Explanation From Exact Extract:

NVIDIADOCA(Data Center Infrastructure-on-a-Chip Architecture) is the software framework designed to program NVIDIA BlueField DPUs (Data Processing Units). DOCA provides libraries, APIs, and tools to develop custom applications, enabling users to offload, accelerate, and secure data center infrastructure functions on BlueField DPUs.

DOCA supports integration with key data center services including storage protocols such asNVMe over Fabrics (NVMe-oF), elastic block storage, and network security and telemetry. It enables tailored solutions optimized for specific workloads and high-performance infrastructure demands.

TensorRT is focused on AI inference optimization.

CUDA is NVIDIA’s GPU programming model for general-purpose GPU computing, not for DPUs.

NSight is a development environment for debugging and profiling NVIDIA GPUs.

Therefore,NVIDIA DOCAis the correct framework for programming BlueField DPUs in a data center environment requiring custom application development and advanced storage/networking integration.

Comprehensive and Detailed Explanation From Exact Extract:

In Slurm GPU resource management, thegres.conffile defines the available GPUs (generic resources) per node, whileslurm.confconfigures the cluster-wide GPU scheduling policies. To prevent jobs from using GPUs reserved for other purposes (e.g., display rendering GPUs), administrators must ensure that only the GPUs intended for compute workloads are listed in these configuration files.

Properly configuringgres.confallows Slurm to recognize and expose only those GPUs meant for jobs.

slurm.confmust be aligned to exclude or restrict unconfigured GPUs.

Manual GPU assignment usingnvidia-smiis not scalable or integrated with Slurm scheduling.

Reinstalling drivers or increasing GPU requests does not solve resource exclusion.

Thus, the correct approach is to verify and configure GPU listings accurately ingres.confandslurm.confto restrict job allocations to intended GPUs.

=============

Comprehensive and Detailed Explanation From Exact Extract:

The Slurm commandsstatis designed to provide real-time statistics about running jobs, including process-level details and resource usage such as CPU, memory, and GPU utilization. Usingsstat -j orsstat -j allows monitoring of active job resource consumption.

smapis not a standard Slurm command.

scontrol show jobgives job configuration and status but not real-time resource usage.

sinfodisplays node and partition information, not job-specific resource stats.

Therefore,sstatis the correct command for real-time job process and resource monitoring.

Comprehensive and Detailed Explanation From Exact Extract:

TheFleet Command Operatorrole is designed to provide users with read-only access to locations and dashboards while granting full read/write/admin rights for applications and deployments. This matches the described access requirements where the user can manage applications and deployments but only view locations and dashboards without modification rights. Other roles like Fleet Command Admin have broader permissions, Supporter has more limited access, and Viewer is primarily read-only for all resources.

=============

Comprehensive and Detailed Explanation From Exact Extract:

In Run:ai, roles are assigned based on levels of permissions. TheL1 Researcherrole is designed for users who need access to GPU resources for running jobs and experiments but should not have administrative rights over cluster settings or other users. This role ensures researchers can use resources without affecting cluster configurations or user management. Other roles like Department Administrator, Application Administrator, or Research Manager have broader privileges, including managing users and settings, which are not appropriate for the new researcher’s requirements.

=============