NVIDIA NCP-AII Questions & Answers

That change may help in a different context, but it would not establish that Each installed cable part number matches the approved link budget and passes physical and electrical checks. The relevant response is Inventory actual cable media, supported distance, vendor compatibility and bend requirements by link.

That change may help in a different context, but it would not establish that Each installed cable part number matches the approved link budget and passes physical and electrical checks. The relevant response is Inventory actual cable media, supported distance, vendor compatibility and bend requirements by link.

The related but distinct situation is “An engineer installs an optic that matches the physical cage but uses a wavelength incompatible with the remote transceiver”. The proposed repair must address the observed dependency: Matching connector geometry does not imply optical signaling compatibility between transceiver endpoints. For the stated problem, “Inventory actual cable media, supported distance, vendor compatibility and bend requirements by link” addresses the central condition.

The option would apply to “An AI inference server performs correctly at one request per second but misses latency targets when all accelerators share the CPU and network paths”. Without correction, Single-request smoke tests can conceal contention and poor placement under production demand. The present scenario is distinguished by “Unexpected resource partitioning can prevent large models from loading even on powerful hardware”.

The related but distinct situation is “A node passes short GPU tests but its application intermittently throttles after twenty minutes of sustained work”. Without correction, Short acceptance tests can conceal thermally constrained GPUs that disrupt production jobs. The deciding requirement here is “Unexpected resource partitioning can prevent large models from loading even on powerful hardware”.

This option serves the scenario “A GPU node's driver initializes but the cluster image lacks the libraries used by the production training framework”. The direct failure mode is that Unvalidated user-space dependencies can cause failures after jobs are distributed to the cluster. This question instead requires “Unexpected resource partitioning can prevent large models from loading even on powerful hardware”.

The proposed validation does not isolate the reported fault. An effective closure test requires evidence that Post-update health reports show correctly registered nodes and successful GPU container jobs.

The candidate evidence checks Correct the BCM category or node-specific settings for the intended fabric and shared storage before reprovisioning in another context. Here the specific acceptance result is Post-update health reports show correctly registered nodes and successful GPU container jobs.

The proposed validation does not isolate the reported fault. An effective closure test requires evidence that Post-update health reports show correctly registered nodes and successful GPU container jobs.

This option serves the scenario “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. This question instead requires “Visible NVIDIA devices do not guarantee that the application framework contains GPU-enabled binaries”.

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 operational evidence follows from the mechanism that Excessive GPU enumeration can undermine resource allocation and tenant isolation on shared servers. For the stated problem, “Visible NVIDIA devices do not guarantee that the application framework contains GPU-enabled binaries” 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 controlling explanation is that Container images do not virtualize away the host driver compatibility requirements of GPU workloads. The present scenario is distinguished by “Visible NVIDIA devices do not guarantee that the application framework contains GPU-enabled binaries”.

This option serves the scenario “A freshly installed GPU appears under lspci but fans spike and its temperature rises as soon as workload memory is allocated”. The direct fix is Inspect GPU mounting, cooling interface and airflow before repeating controlled load tests. The present scenario is distinguished by “Check approved accelerator SKU, platform power profile and thermal compatibility before reintroducing workloads”.

This option addresses an alternative scenario (A technician installs a PCIe accelerator into a slot wired with too few lanes and assumes the same bandwidth as the reference GPU server); the reported issue instead calls for Check approved accelerator SKU, platform power profile and thermal compatibility before reintroducing workloads.

The related but distinct situation is “A service team replaces a failed accelerator without isolating power or following ESD safeguards specified for the chassis”. The appropriate intervention is Perform approved powered-down field replacement using ESD protection and correct retaining and thermal hardware. For the stated problem, “Check approved accelerator SKU, platform power profile and thermal compatibility before reintroducing workloads” addresses the central condition.

The suggested action targets another problem where Host-side CPU performance and power policy can bound GPU data supply even when CUDA compute is healthy. It does not directly address this case, which needs Rebaseline processor and memory configuration using platform-specific NUMA, PCIe and BIOS guidance.

The option would apply to “A GPU data loader on a dual-socket AMD server places CPU worker threads far from the PCIe root complex hosting the selected GPU”. The direct fix is Profile and align CPU affinity and NUMA memory placement to the GPU and NIC topology. The present scenario is distinguished by “Rebaseline processor and memory configuration using platform-specific NUMA, PCIe and BIOS guidance”.

The related but distinct situation is “An engineer enables every possible CPU thread for data loading without checking memory locality and context-switch overhead on a multi-socket host”. The appropriate intervention is Measure thread scaling, memory bandwidth and scheduler affinity to choose a balanced data-preprocessing configuration. The deciding requirement here is “Rebaseline processor and memory configuration using platform-specific NUMA, PCIe and BIOS guidance”.

This is weaker evidence because Container images do not virtualize away the host driver compatibility requirements of GPU workloads. The independent test must show that The selected image passes an actual CUDA kernel run using the installed host driver.

This option serves the scenario “A privileged Docker container succeeds with GPU access while an ordinary GPU-enabled container fails the same workload”. The strongest acceptance result is that The restricted container runs the CUDA sample with only the intended GPU and without privileged mode, not merely that a command executed. The present scenario is distinguished by “The selected image passes an actual CUDA kernel run using the installed host driver”.

The proposed validation does not isolate the reported fault. An effective closure test requires evidence that The selected image passes an actual CUDA kernel run using the installed host driver.

The related but distinct situation is “A cluster node has an appropriate GPU driver but a signed kernel refuses to load the unsigned NVIDIA module under secure boot”. The operational evidence follows from the mechanism that Secure boot enforcement can prevent module loading even when package installation succeeds. The deciding requirement here is “Removing GPU control software from an active compute node can interrupt every GPU process”.

This is an incorrect prioritization of the mechanism. The reported fault points to Removing GPU control software from an active compute node can interrupt every GPU process, not the separate condition involving A host updates the CUDA toolkit but leaves an incompatible NVIDIA kernel driver and the application fails to initialize CUDA.

The option would apply to “A GPU driver update leaves a stale kernel module loaded because the server was not restarted or reloaded safely”. The operational evidence follows from the mechanism that Installed driver packages and the currently loaded kernel module can be different versions. The present scenario is distinguished by “Removing GPU control software from an active compute node can interrupt every GPU process”.

That change may help in a different context, but it would not establish that Host and DPU version inventories agree and a DOCA network function completes its smoke test. The relevant response is Use the supported DOCA host/Arm firmware compatibility matrix and coordinate a staged update.

The suggested action targets another problem where The CUDA toolkit cannot replace the host kernel driver required to control an installed NVIDIA GPU. It does not directly address this case, which needs Use the supported DOCA host/Arm firmware compatibility matrix and coordinate a staged update.

That change may help in a different context, but it would not establish that Host and DPU version inventories agree and a DOCA network function completes its smoke test. The relevant response is Use the supported DOCA host/Arm firmware compatibility matrix and coordinate a staged update.

This alternative addresses the separate case “An HGX server cluster has mixed GPU and BMC firmware revisions after a partial maintenance window, but the team runs burn-in as if versions match”. The decisive evidence is that Post-maintenance readback confirms the approved release matrix and a representative GPU stress test passes. For the stated problem, “The recovered node passes BMC inventory and health checks before a staged return to cluster service” addresses the central condition.

That observation does not establish operating effectiveness of Quarantine the affected node and follow the vendor-supported recovery procedure with preserved maintenance logs. The needed evidence is that The recovered node passes BMC inventory and health checks before a staged return to cluster service.

This option serves the scenario “An engineer updates GPU board firmware outside the planned change window while the cluster scheduler continues submitting training jobs”. The decisive evidence is that The scheduler marks the node unavailable during update and returns it only after validated reboot and smoke test. This question instead requires “The recovered node passes BMC inventory and health checks before a staged return to cluster service”.

The option would apply to “During HPL burn-in one GPU's temperature and power-limit counters increase but the aggregate benchmark still finishes”. A completed remediation must demonstrate that Per-device telemetry stabilizes and repeated HPL throughput remains within approved spread. This question instead requires “Replayed tests with matching versions produce stable, interpretable timing and error data”.

The related but distinct situation is “A one-minute HPL run is used for a rack acceptance contract that requires proving sustained computation under design power”. The decisive evidence is that The HPL campaign maintains correct output and accepted thermal and ECC trends throughout the interval. For the stated problem, “Replayed tests with matching versions produce stable, interpretable timing and error data” addresses the central condition.

This alternative addresses the separate case “An HPL stress campaign produces high performance but occasionally fails validation residuals after several hours”. The strongest acceptance result is that Repeated sustained HPL jobs pass both performance and numerical residual acceptance, not merely that a command executed. The deciding requirement here is “Replayed tests with matching versions produce stable, interpretable timing and error data”.