NVIDIA NCP-AII Questions & Answers

This option serves the scenario “A new multi-tenant inference service assigns a whole GPU to every tiny model despite approved MIG-capable hardware”. This case illustrates that MIG partitions expose isolated compute and memory allocations that can improve utilization for compatible jobs. The present scenario is distinguished by “MIG instance configurations may need recreation after GPU or system resets depending on platform and driver”.

The option would apply to “An administrator enables MIG mode on a supported GPU but creates no GPU or compute instances and the workload sees no device”. This case illustrates that MIG mode enablement alone does not create addressable GPU compute resources for applications. This question instead requires “MIG instance configurations may need recreation after GPU or system resets depending on platform and driver”.

The related but distinct situation is “Two tenants see the same MIG device assignment because an orchestration template reuses one accelerator identifier”. The controlling explanation is that Logical isolation is ineffective if scheduling incorrectly shares the same device instance across untrusted jobs. For the stated problem, “MIG instance configurations may need recreation after GPU or system resets depending on platform and driver” addresses the central condition.

This alternative addresses the separate case “A fabric switch reboot clears a temporary configuration and leaves performance counters and congestion policies different from the acceptance baseline”. The direct fix is Validate persistent configuration and active software state before and after a controlled restart. For the stated problem, “Verify switch model, software and firmware support for each planned port mode before acceptance” addresses the central condition.

The related but distinct situation is “A new switch image is installed on half the GPU fabric while the other half runs a different unsupported network OS release”. The appropriate intervention is Check the supported fabric software compatibility matrix and apply the approved staged version baseline. The deciding requirement here is “Verify switch model, software and firmware support for each planned port mode before acceptance”.

This option serves the scenario “An operator checks spine switches only by ping and does not inspect temperature, transceiver status or port error counters”. Therefore Read switch health, firmware, optical diagnostics and error telemetry as part of fabric validation. This question instead requires “Verify switch model, software and firmware support for each planned port mode before acceptance”.

The candidate evidence checks Validate NVLink or supported intra-node communication using NCCL tests after initial device discovery in another context. Here the specific acceptance result is A representative throughput and latency test meets the approved service target at anticipated concurrency.

The related but distinct situation is “A GPU node's driver initializes but the cluster image lacks the libraries used by the production training framework”. The decisive evidence is that A small framework GPU operation and collective test run successfully from the approved image. For the stated problem, “A representative throughput and latency test meets the approved service target at anticipated concurrency” addresses the central condition.

This alternative addresses the separate case “A server can execute one GPU benchmark but its expected GPU memory capacity is reduced by an unintended MIG partition profile”. A completed remediation must demonstrate that A placement test requests the approved device or MIG profile and receives the expected memory and compute capacity. The deciding requirement here is “A representative throughput and latency test meets the approved service target at anticipated concurrency”.

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. The deciding requirement here is “Inspect GPU mounting, cooling interface and airflow before repeating controlled load tests”.

For this finding, Verify the required supported slot width, lane count, chassis airflow and auxiliary power before enrollment is less direct than Inspect GPU mounting, cooling interface and airflow before repeating controlled load tests, because Mechanical installation quality affects thermal transfer and cannot be proven by PCI enumeration alone.

The option would apply to “A replacement GPU is installed beside older boards with a different supported power profile and the BIOS warns of hardware mismatch”. The direct fix is Check approved accelerator SKU, platform power profile and thermal compatibility before reintroducing workloads. The present scenario is distinguished by “Inspect GPU mounting, cooling interface and airflow before repeating controlled load tests”.

This alternative addresses the separate case “An NCCL burn-in is interrupted by a scheduled switch update and the report falsely labels the hardware rack as failing”. The acceptance board should flag that Uncoordinated burn-in and maintenance can create misleading failure investigations. The deciding requirement here is “An unaddressed straggler can degrade every training step even without explicit job failure”.

The option would apply to “A cluster completes a single multi-node NCCL run but times out after several hours of continuous all-reduce burn-in”. The direct failure mode is that Accepting one short collective run can miss recurring job-killing fabric instability. This question instead requires “An unaddressed straggler can degrade every training step even without explicit job failure”.

The related but distinct situation is “A long NCCL test passes only with two ranks while production jobs regularly use 64 GPUs distributed across multiple racks”. The primary exposure is that A two-rank pass cannot certify distributed collectives at the scale used by customers. For the stated problem, “An unaddressed straggler can degrade every training step even without explicit job failure” addresses the central condition.

The option would apply to “An engineer enables every possible CPU thread for data loading without checking memory locality and context-switch overhead on a multi-socket host”. The decisive evidence is that A worker-count sweep identifies a stable throughput peak and lower input queue latency. The present scenario is distinguished by “A controlled CPU pinning test reduces host-to-GPU transfer time without degrading correctness”.

The related but distinct situation is “A deployment moves from an Intel to an AMD host while preserving all x86 CPU tuning flags without verifying the new platform topology”. A completed remediation must demonstrate that A representative training and communication test establishes improved throughput under the new approved settings. The deciding requirement here is “A controlled CPU pinning test reduces host-to-GPU transfer time without degrading correctness”.

That observation does not establish operating effectiveness of Profile and align CPU affinity and NUMA memory placement to the GPU and NIC topology. The needed evidence is that A controlled CPU pinning test reduces host-to-GPU transfer time without degrading correctness.

For this finding, Inventory actual cable media, supported distance, vendor compatibility and bend requirements by link is less direct than Configure a supported breakout profile and map each physical leg to its intended peer before acceptance, because Breakout cabling and switch logical port allocation must agree for every lane and endpoint.

The suggested action targets another problem where Passive copper, active copper and optical transceivers have distinct reach and signal-integrity envelopes. It does not directly address this case, which needs Configure a supported breakout profile and map each physical leg to its intended peer before acceptance.

This alternative addresses the separate case “A dual-rail rack has all high-speed ports connected but two cables are accidentally crossed between fabrics”. The direct fix is Trace transceiver identities and peer ports to the physical topology diagram before cluster benchmarks. The deciding requirement here is “Configure a supported breakout profile and map each physical leg to its intended peer before acceptance”.

This option serves the scenario “A GPU node fails after switching from an NVIDIA supported driver repository to a distribution package that does not match accelerator requirements”. The operational evidence follows from the mechanism that A driver package name alone does not establish support for the specific accelerator architecture. This question instead requires “Secure boot enforcement can prevent module loading even when package installation succeeds”.

This alternative addresses the separate case “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. For the stated problem, “Secure boot enforcement can prevent module loading even when package installation succeeds” addresses the central condition.

The option would apply to “A technician uninstalls the NVIDIA driver without checking which running jobs and container images depend on it”. The controlling explanation is that Removing GPU control software from an active compute node can interrupt every GPU process. The present scenario is distinguished by “Secure boot enforcement can prevent module loading even when package installation succeeds”.

This alternative addresses the separate case “A fabric acceptance team sees all links up but does not check whether storage ports were accidentally wired to GPU collective switches”. Therefore Use a physical topology reconciliation with port identifiers and approved network role assignments. The deciding requirement here is “Use a per-port peer discovery and rank-to-node cable map to isolate the incorrect physical connection”.

The option would apply to “After replacing several DAC cables, a technician fails to check switch port breakout profiles and the same rack has mismatched lane mappings”. Therefore Reconcile breakout cable legs, configured port modes and server interfaces against the reference topology. This question instead requires “Use a per-port peer discovery and rank-to-node cable map to isolate the incorrect physical connection”.

The related but distinct situation is “A rack diagram claims both InfiniBand rails are independent, but an LLDP or switch inventory shows one server connected twice to rail A”. The appropriate intervention is Trace each cable endpoint against the approved rail map and correct the crossed connection. For the stated problem, “Use a per-port peer discovery and rank-to-node cable map to isolate the incorrect physical connection” addresses the central condition.

This alternative addresses the separate case “A BlueField-3 Arm-side service is stopped during maintenance, after”. The controlling explanation is that In DPU mode embedded function ownership affects how host networking becomes available during initialization. For the stated problem, “Zero-trust restrictions may intentionally block host-side hardware counters or tracing functions” addresses the central condition.

This is an incorrect prioritization of the mechanism. The reported fault points to Zero-trust restrictions may intentionally block host-side hardware counters or tracing functions, not the separate condition involving A tenant host administrator can still flash the BlueField firmware even though the project mandates zero-trust DPU management separation.

This statement could explain another scenario: A BlueField DPU is operating in NIC mode, but the security architect expects the Arm control plane to enforce independent host network policy. In the present case the more specific mechanism is that Zero-trust restrictions may intentionally block host-side hardware counters or tracing functions.

The related but distinct situation is “A NeMo training job processes a single batch successfully but fails during prolonged checkpoint and distributed communication cycles”. The primary exposure is that Synthetic-only smoke tests can miss application-stack integration failures at production duration. For the stated problem, “An inconsistent resume can invalidate benchmark comparisons and corrupt model quality” addresses the central condition.

This option serves the scenario “A NeMo burn-in has good GPU utilization but model checkpoints intermittently fail because shared storage fills during long runs”. The acceptance board should flag that Unobserved checkpoint storage exhaustion can discard hours of otherwise healthy training. The present scenario is distinguished by “An inconsistent resume can invalidate benchmark comparisons and corrupt model quality”.

This alternative addresses the separate case “A NeMo benchmark appears slow while GPU and network metrics remain normal but tokenization input workers are pegged at CPU limits”. The acceptance board should flag that Misdiagnosing CPU preprocessing as GPU failure can lead to unnecessary accelerator replacement. The deciding requirement here is “An inconsistent resume can invalidate benchmark comparisons and corrupt model quality”.