NVIDIA NCP-AIO NVIDIA AI Operations Exam Practice Test

Comprehensive and Detailed Explanation From Exact Extract:

For distributed AI training jobs that require fast inter-node communication, such as those using TensorFlow across multiple GPUs and nodes,InfiniBand networkingis the preferred solution. InfiniBand provides ultra-low latency and high bandwidth, reducing communication delays significantly and increasing overall training throughput. While jumbo frames on Ethernet can help, they do not match the performance of InfiniBand. Dedicated storage networks or increasing replicas do not directly address inter-node communication latency.

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Separating metadata-intensive workloads and large sequential I/O operations ontodifferent storage poolsisolates contention points and optimizes performance for each workload type. Metadata operations benefit from dedicated resources optimized for small, random access, while large sequential I/O requires high-throughput storage. This separation minimizes conflicts and improves overall system responsiveness.

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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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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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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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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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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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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:

Before pulling and running an NVIDIA NGC container on a stand-alone server, you must:

InstallDockerand theNVIDIA Container Toolkitto enable container runtime with GPU support.

Generate anNGC API keyand authenticate with the NGC container registry usingdocker loginto pull private or public containers.

Setting up Kubernetes or manually installing deep learning frameworks is unnecessary when using containers as they include the required frameworks.

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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:

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.

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The Slurm commandscontrol show job provides detailed information about a specific job, including its current status and, crucially, the reason why a job might be pending. This command shows job details such as resource requirements, dependencies, and any issues blocking the job from running.

sinfo -Rdisplays information about nodes and their reasons for being in various states but does not provide job-specific reasons.

sacct -jshows accounting data for jobs but typically does not explain pending causes.

squeue -ulists jobs by user but does not detail the pending reasons.

Hence,scontrol show job is the appropriate command to diagnose why a Slurm job remains in the pending state.

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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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When configuring BCM for cloudbursting across multiple cloud providers such as AWS and Azure, it is necessary toconfigure separate credentials for each cloud providerwithin BCM. This allows BCM to authenticate and manage resources appropriately in each distinct cloud environment. BCM does not automatically replicate or detect credentials, nor can a single credential set typically work across providers.

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The correct command to scale a Kubernetes Job to a specific number of replicas iskubectl scale job --replicas=4. This explicitly sets the number of desired pod instances for the Job resource. The other commands are either invalid (stretch), apply to Deployments rather than Jobs (autoscale deployment), or use incorrect syntax (-r).

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Comprehensive and Detailed Explanation From Exact Extract:

NVIDIA Fabric Manager manages the NVLink and NVSwitch fabric resources within HGX systems, enabling efficient resource allocation, communication, and virtualization necessary for AI/ML workloads. This is critical for virtualization as it ensures optimized interconnect performance between GPUs. Video encoding, graphical rendering, or memory upgrades are outside the scope of Fabric Manager.

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Comprehensive and Detailed Explanation From Exact Extract:

For large-scale AI workloads such as distributed training of large language models, the networking infrastructure must deliver extremely low latency and very high throughput to keep GPUs and compute nodes efficiently synchronized. NVIDIA highlights thatInfiniBand networkingis essential in AI data centers because it provides ultra-low latency, high bandwidth, adaptive routing, congestion control, and noise isolation—features critical for high-performance AI training clusters.

InfiniBand acts not just as a network but as acomputing fabric, integrating compute and communication tightly. Microsoft Azure, a leading cloud provider, uses thousands of miles of InfiniBand cabling to meet the demands of their AI workloads, demonstrating its importance. While Ethernet-based solutions like NVIDIA’s Spectrum-X are emerging and optimized for AI, InfiniBand remains the premier choice for AI supercomputing networks.

Therefore, for maximizing performance in a new AI cluster focused on distributed training,InfiniBand networking (option D)is the recommended architecture. Other Ethernet-based approaches provide scalability and bandwidth but cannot match InfiniBand’s specialized low-latency and high-throughput performance for AI.

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Thedocker logs CONTAINER-NAMEcommand retrieves thestandard output (STDOUT)andstandard error (STDERR)streams of a running or stopped container. It is the primary tool for inspecting container I/O logs for debugging and monitoring purposes.docker topshows running processes,docker statsshows resource usage, anddocker inspectshows metadata/configuration.

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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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