NVIDIA NCP-AII Questions & Answers

This would be a meaningful test for another issue (An AI inference server performs correctly at one request per second but misses latency targets when all accelerators share the CPU and network paths), but not for the required outcome: A small framework GPU operation and collective test run successfully from the approved image.

This option serves the scenario “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 present scenario is distinguished by “A small framework GPU operation and collective test run successfully from the approved image”.

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 small framework GPU operation and collective test run successfully from the approved image.

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 operational evidence follows from the mechanism that Mechanical installation quality affects thermal transfer and cannot be proven by PCI enumeration alone. This question instead requires “Mixed physical GPU variants can violate validated platform configuration or available rack power”.

This alternative addresses the separate case “A service team replaces a failed accelerator without isolating power or following ESD safeguards specified for the chassis”. The operational evidence follows from the mechanism that GPU service safety depends on chassis-specific procedures, grounding and component handling limits. For the stated problem, “Mixed physical GPU variants can violate validated platform configuration or available rack power” addresses the central condition.

The option would apply to “A technician installs a PCIe accelerator into a slot wired with too few lanes and assumes the same bandwidth as the reference GPU server”. This case illustrates that An installed GPU may enumerate even when the physical platform does not supply its expected I/O resources. The present scenario is distinguished by “Mixed physical GPU variants can violate validated platform configuration or available rack power”.

This option serves the scenario “A multi-GPU server repeatedly loses one NIC link when its fan tray slows under load”. Without correction, An untreated cooling defect can cause multiple misleading hardware symptoms and repeated outages. The present scenario is distinguished by “Replacing software to address a facility-induced thermal issue wastes time and leaves equipment at risk”.

The option would apply to “A node experiences intermittent network errors only during peak HPL and NCCL workload overlap, and the team assumes it is a faulty transceiver”. The primary exposure is that A power-delivery fault may survive repeated optic swaps and continue causing rack-wide downtime. This question instead requires “Replacing software to address a facility-induced thermal issue wastes time and leaves equipment at risk”.

The related but distinct situation is “An AI server stalls under load with no GPU errors but system logs show PCIe AER messages on the network adapter slot”. The acceptance board should flag that Misattributing bus errors to application code can prolong the real hardware fault. For the stated problem, “Replacing software to address a facility-induced thermal issue wastes time and leaves equipment at risk” addresses the central condition.

This alternative addresses the separate case “A storage and training network both connect to the same leaf pair, but the architect ignores competing traffic during checkpoint bursts”. The direct fix is Define traffic classes and capacity headroom for storage checkpoint writes and synchronous GPU collectives. For the stated problem, “Choose a validated nonblocking or acceptably oversubscribed leaf-spine data fabric sized for east-west collective traffic” addresses the central condition.

The option would apply to “A new AI factory adds two leaf switches but places their uplinks through a single shared spine and claims full fabric availability”. Therefore Model spine and leaf failures and provide redundant forwarding resources matched to the target recovery level. The present scenario is distinguished by “Choose a validated nonblocking or acceptably oversubscribed leaf-spine data fabric sized for east-west collective traffic”.

For this finding, Create explicit management and data-plane isolation with suitable routing, QoS and access boundaries is less direct than Choose a validated nonblocking or acceptably oversubscribed leaf-spine data fabric sized for east-west collective traffic, because Distributed training creates substantial simultaneous east-west communication that a single congested access uplink can throttle.

This would be a meaningful test for another issue (A Base Command Manager cluster relies on one head node and loses both DHCP provisioning and Slurm scheduling whenever that node powers off), but not for the required outcome: A management-path or feed failure exercise leaves one head capable of taking over essential services.

The related but distinct situation is “A backup head node has no configured remote power-fencing capability, but the team expects automatic recovery from any loss of heartbeat”. The decisive evidence is that A planned failure demonstration follows the documented automatic or manual activation and fencing sequence. For the stated problem, “A management-path or feed failure exercise leaves one head capable of taking over essential services” addresses the central condition.

The candidate evidence checks Configure supported HA fencing, quorum and shared-resource ownership protections before approving automated failover in another context. Here the specific acceptance result is A management-path or feed failure exercise leaves one head capable of taking over essential services.

The related but distinct situation is “A newly installed rack responds on the host NIC but its BMC address is missing from the protected management subnet”. The appropriate intervention is Configure and validate the correct management interface, addressing, gateway, and authenticated IPMI or Redfish access. The deciding requirement here is “Restrict Redfish and IPMI access to the approved management segment and verify TLS and authorization”.

This option serves the scenario “A technician changes the BMC clock manually but never configures time synchronization, causing alert timestamps to disagree with cluster logs”. The appropriate intervention is Configure protected NTP or an approved management time source for BMC and operating system events. This question instead requires “Restrict Redfish and IPMI access to the approved management segment and verify TLS and authorization”.

For this finding, Reconcile BMC MAC identities and serial numbers before assigning unique documented management addresses is less direct than Restrict Redfish and IPMI access to the approved management segment and verify TLS and authorization, because Provisioning convenience does not justify exposing privileged baseboard controllers to broad tenant networks.

For this finding, Inspect thermal sensors, fan health, PCIe and NIC errors together before changing fabric configuration is less direct than Compare error growth against vendor health criteria and isolate the suspect GPU for extended testing, because Rising corrected memory errors may indicate deterioration before uncorrectable failures appear.

The option would apply to “An AI server stalls under load with no GPU errors but system logs show PCIe AER messages on the network adapter slot”. The direct fix is Investigate PCIe link health, slot topology and firmware before assigning the problem to NCCL software. This question instead requires “Compare error growth against vendor health criteria and isolate the suspect GPU for extended testing”.

The related but distinct situation is “The operator sees repeated training slowdowns after a rack cooling change but assumes the NCCL library is defective”. Therefore Compare GPU clock-limit reasons, thermal sensors and fabric telemetry against the prior baseline. For the stated problem, “Compare error growth against vendor health criteria and isolate the suspect GPU for extended testing” addresses the central condition.

This alternative addresses the separate case “An HPL result is announced as peak GPU FLOPS without recording matrix size, precision, GPU count or the HPL implementation”. The direct fix is Record a reproducible HPL benchmark configuration, software version and numeric precision before comparing results. For the stated problem, “Compare per-device utilization, clock limits, PCIe topology and health events before attributing variation to the benchmark” addresses the central condition.

The related but distinct situation is “A new AI rack posts a good HPL score once but fails to reproduce it after system reboot”. Therefore Repeat HPL with fixed settings across controlled restarts and track device and firmware state. The deciding requirement here is “Compare per-device utilization, clock limits, PCIe topology and health events before attributing variation to the benchmark”.

This option serves the scenario “A cluster uses tiny HPL matrices that fit entirely in cache and reports unrealistically high efficiency for the intended training systems”. Therefore Select HPL problem sizes and process topology representative of the resources being validated. This question instead requires “Compare per-device utilization, clock limits, PCIe topology and health events before attributing variation to the benchmark”.

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 “Trace each cable endpoint against the approved rail map and correct the crossed connection”.

This option serves the scenario “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 present scenario is distinguished by “Trace each cable endpoint against the approved rail map and correct the crossed connection”.

This alternative addresses the separate case “A migration swaps two similarly labeled fabric cables and only one of four MPI ranks loses inter-node connectivity”. The direct fix is Use a per-port peer discovery and rank-to-node cable map to isolate the incorrect physical connection. The deciding requirement here is “Trace each cable endpoint against the approved rail map and correct the crossed connection”.

This option serves the scenario “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 “Port software and optics form a combined physical signaling system that should be qualified together”.

This alternative addresses the separate case “An engineer replaces a failed optical module and assumes no post-replacement tests are necessary because the link LED is green”. The key distinction is that A green carrier LED is weaker evidence than validated signal quality at target speed. For the stated problem, “Port software and optics form a combined physical signaling system that should be qualified together” addresses the central condition.

The proposed explanation misses the decisive dependency: Port software and optics form a combined physical signaling system that should be qualified together. Validation would demonstrate that A complete version matrix and sustained traffic test show no optic-related errors across the fabric.

This option serves the scenario “An engineer updates GPU board firmware outside the planned change window while the cluster scheduler continues submitting training jobs”. Without correction, Uncoordinated firmware changes can terminate jobs and produce untraceable performance regressions. The present scenario is distinguished by “Mixed unsupported firmware levels can cause intermittent device initialization and fabric instability”.

The option would apply to “A network adapter shows link up but its firmware was never compared with the approved NVIDIA driver and switch stack”. The direct failure mode is that An incompatible NIC firmware combination can manifest only under collective or high-throughput load. This question instead requires “Mixed unsupported firmware levels can cause intermittent device initialization and fabric instability”.

The related but distinct situation is “A BMC firmware flash process fails halfway through and the technician immediately reboots every chassis in the rack”. The primary exposure is that Broad restart during a failed flash can turn a contained device issue into rack-wide downtime. For the stated problem, “Mixed unsupported firmware levels can cause intermittent device initialization and fabric instability” addresses the central condition.