NVIDIA NCP-AII Questions & Answers

This option serves the scenario “An HPL burn-in on eight GPUs has one device consistently 30 percent slower while others remain close to baseline”. A completed remediation must demonstrate that A corrected run shows the outlier device within the supported performance spread and no hardware errors. The present scenario is distinguished by “Repeated runs pass the benchmark validation residual and show stable device health metrics”.

This alternative addresses the separate case “An engineer compares HPL throughput on two GPU nodes but one run uses different process mapping and memory pressure”. A completed remediation must demonstrate that The two runs use documented identical settings and report results with controlled variance. The deciding requirement here is “Repeated runs pass the benchmark validation residual and show stable device health metrics”.

This would be a meaningful test for another issue (A cluster uses tiny HPL matrices that fit entirely in cache and reports unrealistically high efficiency for the intended training systems), but not for the required outcome: Repeated runs pass the benchmark validation residual and show stable device health metrics.

This alternative addresses the separate case “A Docker workload requires only one accelerator, but the launch command passes all eight GPUs from its shared host”. The primary exposure is that Giving small tenants every GPU can produce resource contention and unintended accelerator access. For the stated problem, “Missing device exposure can silently shift jobs to CPU or cause CUDA initialization errors” addresses the central condition.

The option would apply to “A Docker deployment can see a GPU but its container uses a CUDA userspace image newer than the host driver supports”. The acceptance board should flag that Incompatible image and driver revisions can fail only when the application initializes compute. The present scenario is distinguished by “Missing device exposure can silently shift jobs to CPU or cause CUDA initialization errors”.

The related but distinct situation is “A Docker image starts on a CUDA-capable node but silently loads a CPU-only framework wheel”. The direct failure mode is that CPU-only dependencies can waste accelerator capacity despite a healthy container runtime. The deciding requirement here is “Missing device exposure can silently shift jobs to CPU or cause CUDA initialization errors”.

The option would apply to “A replacement transceiver has the correct vendor SKU but an old firmware image that fails supported link diagnostics”. The operational evidence follows from the mechanism that Part number matching does not by itself prove current operational firmware compatibility. This question instead requires “A green carrier LED is weaker evidence than validated signal quality at target speed”.

This option serves the scenario “A fabric upgrade updates all switch operating systems but leaves pluggable optical modules with known compatibility defects”. This case illustrates that Port software and optics form a combined physical signaling system that should be qualified together. The present scenario is distinguished by “A green carrier LED is weaker evidence than validated signal quality at target speed”.

This statement could explain another scenario: Two otherwise identical optical links use transceiver firmware revisions that negotiate different advertised capabilities on the same switch. In the present case the more specific mechanism is that A green carrier LED is weaker evidence than validated signal quality at target speed.

The related but distinct situation is “A Docker image includes CUDA libraries, but the operator launches it without any GPU device allocation flag and wonders why inference runs on CPU”. The operational evidence follows from the mechanism that Container software packages do not by themselves grant access to host GPU device nodes. The deciding requirement here is “Administrative test containers may pass while production users lack valid device permissions or mounts”.

This alternative addresses the separate case “A Docker image starts on a CUDA-capable node but silently loads a CPU-only framework wheel”. This case illustrates that Visible NVIDIA devices do not guarantee that the application framework contains GPU-enabled binaries. For the stated problem, “Administrative test containers may pass while production users lack valid device permissions or mounts” addresses the central condition.

The option would apply to “A privileged Docker container succeeds with GPU access while an ordinary GPU-enabled container fails the same workload”. The operational evidence follows from the mechanism that Privilege escalation can hide missing runtime integration while exposing unnecessary host resources. The present scenario is distinguished by “Administrative test containers may pass while production users lack valid device permissions or mounts”.

This alternative addresses the separate case “A BCM deployment configures two management heads but both depend on the same power feed and switch without alternate management paths”. Therefore Separate critical HA failure domains across the designed network and power infrastructure. The deciding requirement here is “Validate the supported fencing and power-control prerequisites or select an explicitly managed manual failover”.

The option would apply to “A network partition causes a passive BCM head to see the active head as unreachable, and an operator immediately promotes it without quorum checks”. The appropriate intervention is Use the documented BCM HA quorum and split-brain prevention procedure before changing service ownership. This question instead requires “Validate the supported fencing and power-control prerequisites or select an explicitly managed manual failover”.

The related but distinct situation is “Two BCM head nodes both attempt to mount a shared writable filesystem during a simulated split-brain incident”. The direct fix is Configure supported HA fencing, quorum and shared-resource ownership protections before approving automated failover. For the stated problem, “Validate the supported fencing and power-control prerequisites or select an explicitly managed manual failover” addresses the central condition.

This alternative addresses the separate case “A tenant host administrator can still flash the BlueField firmware even though the project mandates zero-trust DPU management separation”. The decisive evidence is that A host attempt to change restricted firmware or port ownership fails while the authorized DPU manager succeeds. For the stated problem, “A simulated host outage still permits authenticated DPU status retrieval and approved recovery actions” addresses the central condition.

The related but distinct situation is “A BlueField-3 Arm-side service is stopped during maintenance, after which the host's accelerated network path fails to initialize”. A completed remediation must demonstrate that Arm-side health and host-side interface state recover in the expected order with working test traffic. The deciding requirement here is “A simulated host outage still permits authenticated DPU status retrieval and approved recovery actions”.

This option serves the scenario “A BlueField DPU is operating in NIC mode, but the security architect expects the Arm control plane to enforce independent host network policy”. The strongest acceptance result is that An inspected mode report confirms embedded ownership and a policy test denies an unauthorized host flow, not merely that a command executed. This question instead requires “A simulated host outage still permits authenticated DPU status retrieval and approved recovery actions”.

The related but distinct situation is “A node's BIOS reports a NUMA topology different from the approved design after a firmware reset”. The decisive evidence is that Topology reports and controlled bandwidth tests align with the approved baseline after configuration correction. For the stated problem, “Simultaneous GPU memory tests complete within errors and thermal limits across all installed devices” addresses the central condition.

The proposed validation does not isolate the reported fault. An effective closure test requires evidence that Simultaneous GPU memory tests complete within errors and thermal limits across all installed devices.

The candidate evidence checks Verify supported auxiliary power connections, mechanical seating and component health against the service manual in another context. Here the specific acceptance result is Simultaneous GPU memory tests complete within errors and thermal limits across all installed devices.

The suggested action targets another problem where PCIe device discovery does not establish all intended GPU-to-GPU high-bandwidth connections. It does not directly address this case, which needs Separate single-node NVLink performance from inter-node fabric tests before replacing hardware.

For this finding, Check hardware and MIG isolation capabilities and design supported NCCL tests for the actual assigned GPU topology is less direct than Separate single-node NVLink performance from inter-node fabric tests before replacing hardware, because Single-node and cross-node collectives exercise different network paths and failure domains.

The option would apply to “NCCL single-node all-reduce bandwidth falls after maintenance even though each GPU still passes a separate CUDA sample”. The appropriate intervention is Compare GPU topology, NVLink/NVSwitch health and NCCL software versions against the known baseline. The present scenario is distinguished by “Separate single-node NVLink performance from inter-node fabric tests before replacing hardware”.

This option serves the scenario “A failed GPU in a hot-running server is removed while the node remains scheduled for production jobs”. The direct fix is Drain and power down the affected node and follow qualified chassis and ESD service instructions. The present scenario is distinguished by “Select the vendor-approved PSU capacity and verify redundancy and peak accelerator load before return to service”.

The option would apply to “A failed network adapter is replaced but the cluster job scheduler still references the prior interface and HCA identity”. Therefore Update documented adapter identity, firmware, fabric mapping and host/cluster configuration after replacement. This question instead requires “Select the vendor-approved PSU capacity and verify redundancy and peak accelerator load before return to service”.

The related but distinct situation is “A technician replaces a GPU successfully but does not reapply the workload partition profile required for the tenant scheduler”. The appropriate intervention is Restore and validate the approved MIG or allocation configuration after the hardware replacement. For the stated problem, “Select the vendor-approved PSU capacity and verify redundancy and peak accelerator load before return to service” addresses the central condition.

This alternative addresses the separate case “A Slurm controller is upgraded while its compute node daemons and plugins remain at incompatible levels”. The primary exposure is that Uncoordinated upgrades can interrupt all tenants when jobs transition from queue to node. For the stated problem, “An absent training fabric can impose severe communication bottlenecks on distributed jobs” addresses the central condition.

The option would apply to “A training partition accepts jobs from every team even though it is meant for one restricted research group”. The acceptance board should flag that An open scheduler can expose sensitive datasets or monopolize reserved expensive accelerator capacity. The present scenario is distinguished by “An absent training fabric can impose severe communication bottlenecks on distributed jobs”.

The related but distinct situation is “A BCM cluster is ready for general users but no tenant can access the training filesystem from compute nodes”. Without correction, Mismatched identity or file permissions can make queued GPU jobs fail on startup. The deciding requirement here is “An absent training fabric can impose severe communication bottlenecks on distributed jobs”.

The related but distinct situation is “A GPU server's DPU has an approved high-speed physical link but no functioning OOB path for emergency firmware recovery”. The direct failure mode is that Loss of independent management can prolong outages when the DPU blocks host data paths. For the stated problem, “Host privilege reaching DPU firmware can undermine the intended isolation boundary” addresses the central condition.

This option serves the scenario “A BlueField DPU is operating in NIC mode, but the security architect expects the Arm control plane to enforce independent host network policy”. The primary exposure is that Confusing NIC and DPU modes can leave expected independent data-path protection inactive. The present scenario is distinguished by “Host privilege reaching DPU firmware can undermine the intended isolation boundary”.

This alternative overstates a separate issue; the current case should be prioritized for the risk that Host privilege reaching DPU firmware can undermine the intended isolation boundary.