NVIDIA NCA-AIIO Bundle

Not the most direct the action-and-evidence pair for these facts. Maintain an inventory of GPU systems, drivers, firmware, network devices and approved configuration versions with controlled rollout and reconciliation. This choice concerns cluster inventory firmware and configuration baseline and may be valid in another design. But this case reports 'Kubernetes nodes contain GPUs but the scheduler reports zero allocatable GPU resources', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the action-and-evidence pair for these facts. Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs. This choice concerns distributed job placement and topology-aware orchestration and may be valid in another design. But this case reports 'Kubernetes nodes contain GPUs but the scheduler reports zero allocatable GPU resources', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the action-and-evidence pair for these facts. A time-series review connects kernel activity and memory use to actual completed training or inference work while checking limit events. This choice concerns gpu utilization memory and power telemetry interpretation and may be valid in another design. But this case reports 'Kubernetes nodes contain GPUs but the scheduler reports zero allocatable GPU resources', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the corrective action for these facts. Use NVIDIA Data Center GPU Manager to collect supported accelerator telemetry, health and diagnostic information across datacenter GPU nodes. This choice concerns nvidia dcgm gpu fleet diagnostics and health and may be valid in another design. But this case reports 'A training pod lands on a CPU-only node because its manifest lacks a GPU request', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the corrective action for these facts. Use a workload scheduler such as Slurm with explicit job GPU resource requests, queue policies and fair-share controls for shared training clusters. This choice concerns slurm batch queues fair-share and reservations and may be valid in another design. But this case reports 'A training pod lands on a CPU-only node because its manifest lacks a GPU request', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the corrective action for these facts. Use nvidia-smi or supported NVIDIA management interfaces to inspect GPU identification, utilization, memory, temperatures and running processes as an initial node-level diagnostic. This choice concerns nvidia-smi point-in-time gpu operational checks and may be valid in another design. But this case reports 'A training pod lands on a CPU-only node because its manifest lacks a GPU request', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the acceptance evidence for these facts. A sampled node's actual firmware, GPU, driver and fabric settings match the approved inventory or show a tracked exception. This choice concerns cluster inventory firmware and configuration baseline and may be valid in another design. But this case reports 'A training pod lands on a CPU-only node because its manifest lacks a GPU request', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the acceptance evidence for these facts. An authorized operator retrieves board-level health and remote console information over the protected management network while the host OS is unavailable. This choice concerns bmc and out-of-band server health administration and may be valid in another design. But this case reports 'A training pod lands on a CPU-only node because its manifest lacks a GPU request', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the acceptance evidence for these facts. Several representative users submit jobs and the scheduler allocates only authorized GPU resources under the published priority and quota policy. This choice concerns slurm batch queues fair-share and reservations and may be valid in another design. But this case reports 'A training pod lands on a CPU-only node because its manifest lacks a GPU request', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the corrective action for these facts. Compare supported vGPU or time-sliced sharing, passthrough and MIG-backed virtualization according to VM isolation, licensing, performance and hardware support requirements. This choice concerns virtual gpus and gpu sharing approaches and may be valid in another design. But this case reports 'A new cluster deploys workloads before configuring compatible GPU drivers and plugins', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the corrective action for these facts. Use nvidia-smi or supported NVIDIA management interfaces to inspect GPU identification, utilization, memory, temperatures and running processes as an initial node-level diagnostic. This choice concerns nvidia-smi point-in-time gpu operational checks and may be valid in another design. But this case reports 'A new cluster deploys workloads before configuring compatible GPU drivers and plugins', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the corrective action for these facts. Use a workload scheduler such as Slurm with explicit job GPU resource requests, queue policies and fair-share controls for shared training clusters. This choice concerns slurm batch queues fair-share and reservations and may be valid in another design. But this case reports 'A new cluster deploys workloads before configuring compatible GPU drivers and plugins', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the acceptance evidence for these facts. A sampled node's actual firmware, GPU, driver and fabric settings match the approved inventory or show a tracked exception. This choice concerns cluster inventory firmware and configuration baseline and may be valid in another design. But this case reports 'A new cluster deploys workloads before configuring compatible GPU drivers and plugins', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the acceptance evidence for these facts. A multi-node training allocation starts with its required worker set and achieves expected synchronized-step performance without stranded partial jobs. This choice concerns distributed job placement and topology-aware orchestration and may be valid in another design. But this case reports 'A new cluster deploys workloads before configuring compatible GPU drivers and plugins', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the acceptance evidence for these facts. A reproducible fault is linked to device identifiers, event history and a documented diagnostic action with a verified recovery result. This choice concerns gpu reliability events and diagnostic triage and may be valid in another design. But this case reports 'A new cluster deploys workloads before configuring compatible GPU drivers and plugins', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the corrective action for these facts. Use a secured baseboard management controller and out-of-band path to inspect system sensors, power and remote console status independently of the host operating system. This choice concerns bmc and out-of-band server health administration and may be valid in another design. But this case reports 'A team expects the scheduler to grant accelerators automatically from a container image name', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the corrective action for these facts. Use a workload scheduler such as Slurm with explicit job GPU resource requests, queue policies and fair-share controls for shared training clusters. This choice concerns slurm batch queues fair-share and reservations and may be valid in another design. But this case reports 'A team expects the scheduler to grant accelerators automatically from a container image name', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the corrective action for these facts. Use NVIDIA Data Center GPU Manager to collect supported accelerator telemetry, health and diagnostic information across datacenter GPU nodes. This choice concerns nvidia dcgm gpu fleet diagnostics and health and may be valid in another design. But this case reports 'A team expects the scheduler to grant accelerators automatically from a container image name', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the acceptance evidence for these facts. A reproducible fault is linked to device identifiers, event history and a documented diagnostic action with a verified recovery result. This choice concerns gpu reliability events and diagnostic triage and may be valid in another design. But this case reports 'A team expects the scheduler to grant accelerators automatically from a container image name', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the acceptance evidence for these facts. A point-in-time inspection identifies the correct GPU and active process, memory and temperature state corresponding to the reported issue. This choice concerns nvidia-smi point-in-time gpu operational checks and may be valid in another design. But this case reports 'A team expects the scheduler to grant accelerators automatically from a container image name', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the acceptance evidence for these facts. Several representative users submit jobs and the scheduler allocates only authorized GPU resources under the published priority and quota policy. This choice concerns slurm batch queues fair-share and reservations and may be valid in another design. But this case reports 'A team expects the scheduler to grant accelerators automatically from a container image name', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the corrective action for these facts. Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs. This choice concerns distributed job placement and topology-aware orchestration and may be valid in another design. But this case reports 'A node upgrade makes GPU resources disappear from Kubernetes discovery', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the corrective action for these facts. Use a secured baseboard management controller and out-of-band path to inspect system sensors, power and remote console status independently of the host operating system. This choice concerns bmc and out-of-band server health administration and may be valid in another design. But this case reports 'A node upgrade makes GPU resources disappear from Kubernetes discovery', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the corrective action for these facts. Compare supported vGPU or time-sliced sharing, passthrough and MIG-backed virtualization according to VM isolation, licensing, performance and hardware support requirements. This choice concerns virtual gpus and gpu sharing approaches and may be valid in another design. But this case reports 'A node upgrade makes GPU resources disappear from Kubernetes discovery', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the primary risk for these facts. A healthy host does not prove its accelerator is recognized or free of memory pressure and unexpected competing processes. This choice concerns nvidia-smi point-in-time gpu operational checks and may be valid in another design. But this case reports 'A node upgrade makes GPU resources disappear from Kubernetes discovery', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the primary risk for these facts. Uncontrolled first-come submissions and missing resource limits can starve teams or schedule more accelerator work than exists. This choice concerns slurm batch queues fair-share and reservations and may be valid in another design. But this case reports 'A node upgrade makes GPU resources disappear from Kubernetes discovery', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the primary risk for these facts. Inconsistent hardware or firmware versions across nodes can create hard-to-reproduce performance and compatibility failures. This choice concerns cluster inventory firmware and configuration baseline and may be valid in another design. But this case reports 'A node upgrade makes GPU resources disappear from Kubernetes discovery', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the corrective action for these facts. Use a workload scheduler such as Slurm with explicit job GPU resource requests, queue policies and fair-share controls for shared training clusters. This choice concerns slurm batch queues fair-share and reservations and may be valid in another design. But this case reports 'A tenant uses generic CPU requests and unexpectedly competes for an unmanaged GPU', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the corrective action for these facts. Compare supported vGPU or time-sliced sharing, passthrough and MIG-backed virtualization according to VM isolation, licensing, performance and hardware support requirements. This choice concerns virtual gpus and gpu sharing approaches and may be valid in another design. But this case reports 'A tenant uses generic CPU requests and unexpectedly competes for an unmanaged GPU', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the corrective action for these facts. Choose supported Multi-Instance GPU profiles for multiple smaller workloads requiring hardware-partitioned compute and memory isolation, after verifying GPU and software support. This choice concerns nvidia mig hardware partitioning and isolation and may be valid in another design. But this case reports 'A tenant uses generic CPU requests and unexpectedly competes for an unmanaged GPU', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the action-and-evidence pair for these facts. Use a workload scheduler such as Slurm with explicit job GPU resource requests, queue policies and fair-share controls for shared training clusters. This choice concerns slurm batch queues fair-share and reservations and may be valid in another design. But this case reports 'A tenant uses generic CPU requests and unexpectedly competes for an unmanaged GPU', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the action-and-evidence pair for these facts. A deliberately observed GPU condition appears in DCGM telemetry with node and device identity and leads to an actionable diagnostic record. This choice concerns nvidia dcgm gpu fleet diagnostics and health and may be valid in another design. But this case reports 'A tenant uses generic CPU requests and unexpectedly competes for an unmanaged GPU', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.

Not the most direct the action-and-evidence pair for these facts. Use a secured baseboard management controller and out-of-band path to inspect system sensors, power and remote console status independently of the host operating system. This choice concerns bmc and out-of-band server health administration and may be valid in another design. But this case reports 'A tenant uses generic CPU requests and unexpectedly competes for an unmanaged GPU', which calls for reasoning about Kubernetes gpu device scheduling and discovery. The targeted response is: Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices.