NCP-AIO NVIDIA AI Operations Free Practice Exam Questions (2025 Updated)

Comprehensive and Detailed Explanation From Exact Extract:

The NVIDIA Virtual Machine Image (VMI) for machine learning provisioning automatically sets up anUbuntu Serverenvironment with essential components including Docker-CE, NVIDIA Container Toolkit, CSP CLI, NGC CLI, NVIDIA Driver, andRapids—a suite of GPU-accelerated data science and analytics libraries. This comprehensive stack enables immediate development and deployment of ML workloads.

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Comprehensive and Detailed Explanation From Exact Extract:

Before installing new versions of DOCA drivers on NVIDIA BlueField DPUs, it is required touninstall any previous versionsof DOCA drivers to prevent conflicts and ensure a clean upgrade. This ensures that the new installation is not affected by leftover files or configurations from earlier versions. Re-flashing firmware or disabling network interfaces is not always required before every driver installation. Rebooting the host system might be recommended after installation but is not a prerequisite before installing drivers.

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Comprehensive and Detailed Explanation From Exact Extract:

NVIDIA's Multi-Instance GPU (MIG) technology provideshardware-level isolationof critical GPU resources such as memory, cache, and compute units for each GPU instance. This ensures that workloads running in one instance are fully isolated and cannot interfere with the performance of workloads in other instances, supporting multi-tenancy without contention.

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Comprehensive and Detailed Explanation From Exact Extract:

In Kubernetes architecture, thecontrol planeis composed of several core components including thekube-apiserver,etcd(the cluster’s key-value store),kube-scheduler, andkube-controller-manager. These manage the overall cluster state, scheduling, and orchestration of workloads. Theworker nodesare responsible for running the actual containers and include thekubelet(agent that communicates with the control plane) andkube-proxy(handles network routing for services). Other options incorrectly assign these components or roles.

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Comprehensive and Detailed Explanation From Exact Extract:

A best practice for active-passive HA clusters, such as for firewall systems managed via BCM, is touse a heartbeat networkto synchronize session state data between active and passive nodes. This real-time synchronization allows the passive node to take over seamlessly in case the active node fails, maintaining session continuity and minimizing downtime. Configuring different zone names or firewall models can cause incompatibility, and manual synchronization is prone to errors and delays.

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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:

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:

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.

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Comprehensive and Detailed Explanation From Exact Extract:

Disablingswapon all control-plane nodes is a critical prerequisite before initializing Kubernetes control-plane nodes. Kubernetes requires swap to be disabled to maintain performance and stability. Failure to disable swap can cause kubeadm initialization to fail or lead to unpredictable cluster behavior.

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Comprehensive and Detailed Explanation From Exact Extract:

The first step in troubleshooting Kubernetes pods that cannot detect GPUs is to verify whether theNVIDIA GPU Operatoris properly installed and running. The GPU Operator manages the installation and configuration of all NVIDIA GPU components in the cluster, including drivers, device plugins, and monitoring tools. Without it, pods will not have access to GPU resources. Ensuring correct installation and operational status of the GPU Operator is essential before checking application-level versions or resource allocations.

Comprehensive and Detailed Explanation From Exact Extract:

NVIDIA Base Command Manager (BCM) provides aCluster Extension featurethat enables automatic provisioning and scaling of cloud resources (e.g., AWS) when on-premises capacity is fully utilized. This cloudbursting capability allows seamless extension of computational resources without manual intervention, improving flexibility and reducing downtime during peak demand. Options A, B, and C involve manual or incomplete automation approaches that do not leverage BCM’s integrated cluster extension functionality.

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Comprehensive and Detailed Explanation From Exact Extract:

The correctsbatchoption to specify a custom output file for both standard output and error is-o output.txt(or--output=output.txt). This option directs Slurm to write the job’s standard output and error streams to the specified file. The-eoption is for standard error only, and-output-outputis not a valid option.

Comprehensive and Detailed Explanation From Exact Extract:

Slow GPU-to-GPU communication in distributed systems often relates to theconfiguration of communication libraries such as NCCL (NVIDIA Collective Communications Library) or NVSHMEM. Ensuring these libraries are properly configured and optimized is critical for efficient GPU communication. Limiting GPUs or increasing RAM does not directly improve communication speed, and disabling InfiniBand would degrade performance.

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Comprehensive and Detailed Explanation From Exact Extract:

GPUDirect Storage performance relies heavily onRDMA-capable network hardware and proper configurationto enable direct memory access between storage and GPUs, bypassing CPU involvement for faster data transfers. Therefore, the first troubleshooting step should be to verify that RDMA-capable hardware is present and correctly configured. Adjusting GPU clocks, CPU speed, or GPU memory does not address the fundamental networking requirement for GPUDirect Storage.

Comprehensive and Detailed Explanation From Exact Extract:

The first step to troubleshoot Docker container volume mounting issues is tocheck the container logsusingdocker logsfor detailed error messages, including those related to permissions. This provides direct insight into the cause of the failure. Reinstalling Docker or disabling shared folders are drastic steps and may not address the root cause. Volume size reduction is unrelated to permission conflicts.

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Comprehensive and Detailed Explanation From Exact Extract:

When running a GPU-enabled container directly on a server with Docker, the flag--gpus allis required to allow the container access to all GPUs on the host system. This ensures that the TensorFlow container can utilize GPU resources fully. The other options either do not specify GPU access correctly or are Kubernetes-specific commands.

Comprehensive and Detailed Explanation From Exact Extract:

To prioritize inference workloads over training jobs in Kubernetes, administrators should configurePriorityClassesandResourceQuotas. 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.

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Comprehensive and Detailed Explanation From Exact Extract:

TheNVIDIA Data Center GPU Manager (DCGM)is the comprehensive management tool that provides all the requested functionalities: monitoring GPU behavior, managing configurations, enforcing policies, health diagnostics, process accounting, and NVSwitch monitoring. DCGM is designed for large-scale GPU management in data centers and AI clusters, providing detailed telemetry and control over NVIDIA GPUs and NVSwitches.

nvidia-smiprovides GPU monitoring but lacks full policy and NVSwitch management.

CUDA Toolkit is for GPU programming and development.

Nsight Systems is focused on performance profiling and debugging.

Therefore, DCGM is the single tool that meets all the listed requirements.