NVIDIA NCA-AIIO Bundle

Not the most direct the action-and-evidence pair 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 'An engineer enables MIG on a GPU model whose capability was never verified', which calls for reasoning about nvidia mig hardware partitioning and isolation. The targeted response is: Choose supported Multi-Instance GPU profiles for multiple smaller workloads requiring hardware-partitioned compute and memory isolation, after verifying GPU and software support.

Not the most direct the action-and-evidence pair 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 'An engineer enables MIG on a GPU model whose capability was never verified', which calls for reasoning about nvidia mig hardware partitioning and isolation. The targeted response is: Choose supported Multi-Instance GPU profiles for multiple smaller workloads requiring hardware-partitioned compute and memory isolation, after verifying GPU and software support.

Not the most direct the action-and-evidence pair for these facts. Interpret GPU compute utilization, device-memory occupancy, power draw and temperature together over time instead of using one metric as proof of efficient execution. This choice concerns gpu utilization memory and power telemetry interpretation and may be valid in another design. But this case reports 'An engineer enables MIG on a GPU model whose capability was never verified', which calls for reasoning about nvidia mig hardware partitioning and isolation. The targeted response is: Choose supported Multi-Instance GPU profiles for multiple smaller workloads requiring hardware-partitioned compute and memory isolation, after verifying GPU and software support.

Not the most direct the corrective action for these facts. Correlate supported GPU error counters and Xid or ECC events with driver logs, diagnostics and affected workloads before deciding on remediation or hardware replacement. This choice concerns gpu reliability events and diagnostic triage and may be valid in another design. But this case reports 'The cluster requires dedicated slices but all jobs currently compete for the same complete device', which calls for reasoning about nvidia mig hardware partitioning and isolation. The targeted response is: Choose supported Multi-Instance GPU profiles for multiple smaller workloads requiring hardware-partitioned compute and memory isolation, after verifying GPU and software support.

Not the most direct the corrective action for these facts. Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices. This choice concerns Kubernetes gpu device scheduling and discovery and may be valid in another design. But this case reports 'The cluster requires dedicated slices but all jobs currently compete for the same complete device', which calls for reasoning about nvidia mig hardware partitioning and isolation. The targeted response is: Choose supported Multi-Instance GPU profiles for multiple smaller workloads requiring hardware-partitioned compute and memory isolation, after verifying GPU and software support.

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 'The cluster requires dedicated slices but all jobs currently compete for the same complete device', which calls for reasoning about nvidia mig hardware partitioning and isolation. The targeted response is: Choose supported Multi-Instance GPU profiles for multiple smaller workloads requiring hardware-partitioned compute and memory isolation, after verifying GPU and software support.

Not the most direct the acceptance evidence 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 'The cluster requires dedicated slices but all jobs currently compete for the same complete device', which calls for reasoning about nvidia mig hardware partitioning and isolation. The targeted response is: Choose supported Multi-Instance GPU profiles for multiple smaller workloads requiring hardware-partitioned compute and memory isolation, after verifying GPU and software support.

Not the most direct the acceptance evidence for these facts. An authorized VM sees the expected supported accelerator profile and passes workload and isolation tests under simultaneous tenant demand. This choice concerns virtual gpus and gpu sharing approaches and may be valid in another design. But this case reports 'The cluster requires dedicated slices but all jobs currently compete for the same complete device', which calls for reasoning about nvidia mig hardware partitioning and isolation. The targeted response is: Choose supported Multi-Instance GPU profiles for multiple smaller workloads requiring hardware-partitioned compute and memory isolation, after verifying GPU and software support.

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 'The cluster requires dedicated slices but all jobs currently compete for the same complete device', which calls for reasoning about nvidia mig hardware partitioning and isolation. The targeted response is: Choose supported Multi-Instance GPU profiles for multiple smaller workloads requiring hardware-partitioned compute and memory isolation, after verifying GPU and software support.

Not the most direct the corrective action for these facts. Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices. This choice concerns Kubernetes gpu device scheduling and discovery and may be valid in another design. But this case reports 'A scheduling plan assumes MIG profiles can be changed without affecting active dependent jobs', which calls for reasoning about nvidia mig hardware partitioning and isolation. The targeted response is: Choose supported Multi-Instance GPU profiles for multiple smaller workloads requiring hardware-partitioned compute and memory isolation, after verifying GPU and software support.

Not the most direct the corrective action 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 'A scheduling plan assumes MIG profiles can be changed without affecting active dependent jobs', which calls for reasoning about nvidia mig hardware partitioning and isolation. The targeted response is: Choose supported Multi-Instance GPU profiles for multiple smaller workloads requiring hardware-partitioned compute and memory isolation, after verifying GPU and software support.

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 scheduling plan assumes MIG profiles can be changed without affecting active dependent jobs', which calls for reasoning about nvidia mig hardware partitioning and isolation. The targeted response is: Choose supported Multi-Instance GPU profiles for multiple smaller workloads requiring hardware-partitioned compute and memory isolation, after verifying GPU and software support.

Not the most direct the acceptance evidence for these facts. A test pod requests a supported accelerator resource, schedules to an eligible node and performs a successful device computation. This choice concerns Kubernetes gpu device scheduling and discovery and may be valid in another design. But this case reports 'A scheduling plan assumes MIG profiles can be changed without affecting active dependent jobs', which calls for reasoning about nvidia mig hardware partitioning and isolation. The targeted response is: Choose supported Multi-Instance GPU profiles for multiple smaller workloads requiring hardware-partitioned compute and memory isolation, after verifying GPU and software support.

Not the most direct the acceptance evidence for these facts. An authorized VM sees the expected supported accelerator profile and passes workload and isolation tests under simultaneous tenant demand. This choice concerns virtual gpus and gpu sharing approaches and may be valid in another design. But this case reports 'A scheduling plan assumes MIG profiles can be changed without affecting active dependent jobs', which calls for reasoning about nvidia mig hardware partitioning and isolation. The targeted response is: Choose supported Multi-Instance GPU profiles for multiple smaller workloads requiring hardware-partitioned compute and memory isolation, after verifying GPU and software support.

Not the most direct the acceptance evidence 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 scheduling plan assumes MIG profiles can be changed without affecting active dependent jobs', which calls for reasoning about nvidia mig hardware partitioning and isolation. The targeted response is: Choose supported Multi-Instance GPU profiles for multiple smaller workloads requiring hardware-partitioned compute and memory isolation, after verifying GPU and software support.

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 customer requests hardware partition guarantees rather than merely scheduling separate processes', which calls for reasoning about nvidia mig hardware partitioning and isolation. The targeted response is: Choose supported Multi-Instance GPU profiles for multiple smaller workloads requiring hardware-partitioned compute and memory isolation, after verifying GPU and software support.

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 customer requests hardware partition guarantees rather than merely scheduling separate processes', which calls for reasoning about nvidia mig hardware partitioning and isolation. The targeted response is: Choose supported Multi-Instance GPU profiles for multiple smaller workloads requiring hardware-partitioned compute and memory isolation, after verifying GPU and software support.

Not the most direct the corrective action for these facts. Correlate supported GPU error counters and Xid or ECC events with driver logs, diagnostics and affected workloads before deciding on remediation or hardware replacement. This choice concerns gpu reliability events and diagnostic triage and may be valid in another design. But this case reports 'A customer requests hardware partition guarantees rather than merely scheduling separate processes', which calls for reasoning about nvidia mig hardware partitioning and isolation. The targeted response is: Choose supported Multi-Instance GPU profiles for multiple smaller workloads requiring hardware-partitioned compute and memory isolation, after verifying GPU and software support.

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 customer requests hardware partition guarantees rather than merely scheduling separate processes', which calls for reasoning about nvidia mig hardware partitioning and isolation. The targeted response is: Choose supported Multi-Instance GPU profiles for multiple smaller workloads requiring hardware-partitioned compute and memory isolation, after verifying GPU and software support.

Not the most direct the acceptance evidence 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 'A customer requests hardware partition guarantees rather than merely scheduling separate processes', which calls for reasoning about nvidia mig hardware partitioning and isolation. The targeted response is: Choose supported Multi-Instance GPU profiles for multiple smaller workloads requiring hardware-partitioned compute and memory isolation, after verifying GPU and software support.

Not the most direct the acceptance evidence for these facts. An authorized VM sees the expected supported accelerator profile and passes workload and isolation tests under simultaneous tenant demand. This choice concerns virtual gpus and gpu sharing approaches and may be valid in another design. But this case reports 'A customer requests hardware partition guarantees rather than merely scheduling separate processes', which calls for reasoning about nvidia mig hardware partitioning and isolation. The targeted response is: Choose supported Multi-Instance GPU profiles for multiple smaller workloads requiring hardware-partitioned compute and memory isolation, after verifying GPU and software support.

Not the most direct the corrective action for these facts. Correlate supported GPU error counters and Xid or ECC events with driver logs, diagnostics and affected workloads before deciding on remediation or hardware replacement. This choice concerns gpu reliability events and diagnostic triage and may be valid in another design. But this case reports 'Multiple virtual machines need GPU access but the host is configured only for full device passthrough', which calls for reasoning about virtual gpus and gpu sharing approaches. The targeted response is: Compare supported vGPU or time-sliced sharing, passthrough and MIG-backed virtualization according to VM isolation, licensing, performance and hardware support requirements.

Not the most direct the corrective action for these facts. Interpret GPU compute utilization, device-memory occupancy, power draw and temperature together over time instead of using one metric as proof of efficient execution. This choice concerns gpu utilization memory and power telemetry interpretation and may be valid in another design. But this case reports 'Multiple virtual machines need GPU access but the host is configured only for full device passthrough', which calls for reasoning about virtual gpus and gpu sharing approaches. The targeted response is: Compare supported vGPU or time-sliced sharing, passthrough and MIG-backed virtualization according to VM isolation, licensing, performance and hardware support requirements.

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 'Multiple virtual machines need GPU access but the host is configured only for full device passthrough', which calls for reasoning about virtual gpus and gpu sharing approaches. The targeted response is: Compare supported vGPU or time-sliced sharing, passthrough and MIG-backed virtualization according to VM isolation, licensing, performance and hardware support requirements.

Not the most direct the primary risk for these facts. Without a partitioning plan, tenants can contend for full-GPU resources or receive partitions too small for their model memory needs. This choice concerns nvidia mig hardware partitioning and isolation and may be valid in another design. But this case reports 'Multiple virtual machines need GPU access but the host is configured only for full device passthrough', which calls for reasoning about virtual gpus and gpu sharing approaches. The targeted response is: Compare supported vGPU or time-sliced sharing, passthrough and MIG-backed virtualization according to VM isolation, licensing, performance and hardware support requirements.

Not the most direct the primary risk for these facts. Relying only on the workload OS for management can prevent diagnosis or recovery when a node crashes or its production network fails. This choice concerns bmc and out-of-band server health administration and may be valid in another design. But this case reports 'Multiple virtual machines need GPU access but the host is configured only for full device passthrough', which calls for reasoning about virtual gpus and gpu sharing approaches. The targeted response is: Compare supported vGPU or time-sliced sharing, passthrough and MIG-backed virtualization according to VM isolation, licensing, performance and hardware support requirements.

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 'Multiple virtual machines need GPU access but the host is configured only for full device passthrough', which calls for reasoning about virtual gpus and gpu sharing approaches. The targeted response is: Compare supported vGPU or time-sliced sharing, passthrough and MIG-backed virtualization according to VM isolation, licensing, performance and hardware support requirements.

Not the most direct the corrective action for these facts. Correlate supported GPU error counters and Xid or ECC events with driver logs, diagnostics and affected workloads before deciding on remediation or hardware replacement. This choice concerns gpu reliability events and diagnostic triage and may be valid in another design. But this case reports 'A tenant expects exclusive GPU performance from a best-effort time-sliced configuration', which calls for reasoning about virtual gpus and gpu sharing approaches. The targeted response is: Compare supported vGPU or time-sliced sharing, passthrough and MIG-backed virtualization according to VM isolation, licensing, performance and hardware support requirements.

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

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 expects exclusive GPU performance from a best-effort time-sliced configuration', which calls for reasoning about virtual gpus and gpu sharing approaches. The targeted response is: Compare supported vGPU or time-sliced sharing, passthrough and MIG-backed virtualization according to VM isolation, licensing, performance and hardware support requirements.

Not the most direct the action-and-evidence pair 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 tenant expects exclusive GPU performance from a best-effort time-sliced configuration', which calls for reasoning about virtual gpus and gpu sharing approaches. The targeted response is: Compare supported vGPU or time-sliced sharing, passthrough and MIG-backed virtualization according to VM isolation, licensing, performance and hardware support requirements.

Not the most direct the action-and-evidence pair 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 expects exclusive GPU performance from a best-effort time-sliced configuration', which calls for reasoning about virtual gpus and gpu sharing approaches. The targeted response is: Compare supported vGPU or time-sliced sharing, passthrough and MIG-backed virtualization according to VM isolation, licensing, performance and hardware support requirements.

Not the most direct the action-and-evidence pair 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 tenant expects exclusive GPU performance from a best-effort time-sliced configuration', which calls for reasoning about virtual gpus and gpu sharing approaches. The targeted response is: Compare supported vGPU or time-sliced sharing, passthrough and MIG-backed virtualization according to VM isolation, licensing, performance and hardware support requirements.