NCP-AIO Questions and Answers

Comprehensive and Detailed Explanation From Exact Extract:

To optimize scheduling of multi-GPU jobs in Slurm, it is essential to correctly specify GPU requests in job scripts using--gres=gpu:and enable/configureSlurm’s backfill scheduler. Backfill allows smaller jobs to run opportunistically in gaps without delaying larger multi-GPU jobs, improving cluster utilization and reducing wait times for multi-GPU jobs. Proper configuration ensures efficient packing and priority handling of GPU resources.

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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:

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.

Comprehensive and Detailed Explanation From Exact Extract:

To ensure continued operation of AI applications at the edge despite server failures, NVIDIA Fleet Command allows administrators toenable high availability (HA) for edge clusters. This HA configuration ensures redundancy and failover capabilities, so applications remain operational when an edge server goes down.

Over-the-air updates handle software patching but do not inherently provide failover. MIG manages GPU resource partitioning, not failover. Secure NFS supports storage redundancy but is not the primary solution for application failover.

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Comprehensive and Detailed Explanation From Exact Extract:

In an HA cluster, thefailover mechanismis responsible for detecting node failures and automatically transferring workloads to a standby or redundant node to maintain service availability. This process ensures mission-critical applications continue running without interruption. Load balancing helps distribute traffic but does not handle node failures. Manual intervention is not ideal for HA, and data replication ensures data integrity but does not itself manage workload continuity.

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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 standard method to verify that worker nodes are correctly registered and ready in a Kubernetes cluster is to runkubectl get nodes. This command lists all nodes and their statuses. Nodes showing a status of“Ready”indicates they are properly connected and available to schedule workloads. Checking pods or manual SSH is not the direct or reliable way to verify node readiness.

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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:

Core dumps capture thememory state of a process at the time of its crash, providing a snapshot useful for post-mortem debugging. Analyzing core dumps helps identify the cause of segmentation faults or other critical errors by revealing what the process was doing at failure, including stack traces, variable states, and memory content.

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Comprehensive and Detailed Explanation From Exact Extract:

The pre-configured GPU Operator in the NVIDIA Enterprise Catalog differs from the public NGC catalog GPU Operator primarily by its configuration to use aprebuilt vGPU driver imageand being configured to use theNVIDIA License System (NLS). These adaptations allow better support for enterprise environments where vGPU functionality and license management are critical.

Other options such as automatic installation of the Datacenter driver or additional installation of Network Operator are not specific differences highlighted between the two operators.

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Comprehensive and Detailed Explanation From Exact Extract:

The Base Command Manager command shell (cmsh) accepts the-cflag to execute multiple commands sequentially. Usingcmsh -c “main showprofile; device status apc01”runs themain showprofilefollowed bydevice status apc01commands in one invocation, allowing scripted or batch execution from the management node shell.

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Comprehensive and Detailed Explanation From Exact Extract:

TheUFM Telemetryplatform provides basic network monitoring and validation capabilities, making it suitable for organizations that require foundational insight into their network status without advanced analytics or AI-driven cybersecurity features. Other platforms such as UFM Enterprise or UFM Pro offer broader or more advanced functionalities, while UFM Cyber-AI focuses on AI-driven cybersecurity.

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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:

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.