McAfee-Secured Website

Exam Code: NCP-AII

Exam Name: NCP-AI Infrastructure

Certification Provider: NVIDIA

Questions & Answers

100% NVIDIA NCP-AII Guaranteed Success With Testking Training Materials

Get Real NCP-AII Exam Questions & Expert Verified Answers as Experienced on the Real Exam!

Questions & Answers

170 Questions & Answers with Testing Engine

"NCP-AI Infrastructure Exam", also known as NCP-AII exam, is a NVIDIA certification exam.

Pass your tests with the always up-to-date NCP-AII Exam Engine. Your NCP-AII training materials keep you at the head of the pack!

Satisfaction Guaranteed

Money Back Guarantee

Testking provides hassle-free money back guarantee with our products. That is because we have 100% trust in the abilities of our professional and experience product team, and our record is a proof of that.

99.6% PASS RATE
Was: $137.49
Now: $124.99

NVIDIA NCP-AII Practice Test 8

Question 70

An infrastructure administrator receives the following incident: A node intermittently disappears during large CUDA workloads, but the only retained evidence is the operating-system kernel log after it has rebooted. Which measured result most directly proves that this specific problem is resolved?

  1. A repeated failure correlates a device or thermal event across independent management logs and host telemetry with preserved timestamps.
  2. Cross-cohort diagnostics match approved versions and a distributed collective test no longer reports firmware-associated communication instability.
  3. Restoring a validated compatibility setting returns expected PCIe link width without replacing a healthy accelerator.
  4. A sustained memory test produces no unacceptable ECC trend and the suspect component is repaired or replaced with a documented result.

Explanation

Correct Answer

A:

This is the measurable acceptance result for the described case: A repeated failure correlates a device or thermal event across independent management logs and host telemetry with preserved timestamps. It validates the corrective operation (Collect BMC event logs, NVSM or equivalent platform health data, and firmware fault counters before resetting the suspect node.) rather than a related but different system check.

Incorrect Answers

B:

This observation would help verify a different incident: Firmware is upgraded on only half of the HGX nodes, after which GPU fabric health alerts appear only when training jobs span both firmware cohorts. It does not directly establish the required result for the current case: A repeated failure correlates a device or thermal event across independent management logs and host telemetry with preserved timestamps.

C:

This observation would help verify a different incident: A GPU server presents PCIe link-width degradation immediately after a platform firmware change, but technicians plan to replace the accelerator first. It does not directly establish the required result for the current case: A repeated failure correlates a device or thermal event across independent management logs and host telemetry with preserved timestamps.

D:

This observation would help verify a different incident: A GPU host is labeled fully functional based on a POST screen even though one DIMM records recurrent correctable ECC faults under memory load. It does not directly establish the required result for the current case: A repeated failure correlates a device or thermal event across independent management logs and host telemetry with preserved timestamps.

Question 71

An infrastructure administrator receives the following incident: A node intermittently disappears during large CUDA workloads, but the only retained evidence is the operating-system kernel log after it has rebooted. Which root cause or infrastructure principle best explains the observed failure?

  1. Hardware-level errors may survive or precede OS logging and require out-of-band diagnostic evidence for root cause.
  2. Parallel management-controller upgrades enlarge the blast radius of an otherwise localized firmware defect.
  3. Firmware and platform configuration can produce hardware-like symptoms that do not prove physical GPU failure.
  4. Address collisions in the management network make remote recovery and diagnostic attribution unreliable.

Explanation

Correct Answer

A:

This technical mechanism explains the reported condition: Hardware-level errors may survive or precede OS logging and require out-of-band diagnostic evidence for root cause. The observed situation is: A node intermittently disappears during large CUDA workloads, but the only retained evidence is the operating-system kernel log after it has rebooted. A requirement-aligned correction is: Collect BMC event logs, NVSM or equivalent platform health data, and firmware fault counters before resetting the suspect node.

Incorrect Answers

B:

This underlying cause is associated with another GPU platform incident: An operator initiates simultaneous BMC firmware updates on every host, eliminating the team's ability to recover remote power controls if the update fails. The observed case instead concerns: Hardware-level errors may survive or precede OS logging and require out-of-band diagnostic evidence for root cause.

C:

This underlying cause is associated with another GPU platform incident: A GPU server presents PCIe link-width degradation immediately after a platform firmware change, but technicians plan to replace the accelerator first. The observed case instead concerns: Hardware-level errors may survive or precede OS logging and require out-of-band diagnostic evidence for root cause.

D:

This underlying cause is associated with another GPU platform incident: Two BMC controllers ship with duplicate static management addresses and the cluster monitoring system alternately attributes temperature alarms to the wrong chassis. The observed case instead concerns: Hardware-level errors may survive or precede OS logging and require out-of-band diagnostic evidence for root cause.

Question 72

An infrastructure administrator receives the following incident: A node intermittently disappears during large CUDA workloads, but the only retained evidence is the operating-system kernel log after it has rebooted. Which repeatable operational safeguard best prevents recurrence of this failure?

  1. Use staged firmware maintenance, image integrity checks, and verified recovery procedures for BMC updates.
  2. Capture pre- and post-update slot, link, and device health baselines for every firmware rollout.
  3. Continuously retain synchronized management-controller fault logs and automate export before host remediation.
  4. Define ECC threshold monitoring and memory test criteria in the standard hardware validation plan.

Explanation

Correct Answer

C:

This preventive practice addresses the underlying failure (Hardware-level errors may survive or precede OS logging and require out-of-band diagnostic evidence for root cause.) and supports continued assurance through: A repeated failure correlates a device or thermal event across independent management logs and host telemetry with preserved timestamps.

Incorrect Answers

A:

This prevention control targets another operational risk: An operator initiates simultaneous BMC firmware updates on every host, eliminating the team's ability to recover remote power controls if the update fails. The currently reported mechanism remains: Hardware-level errors may survive or precede OS logging and require out-of-band diagnostic evidence for root cause.

B:

This prevention control targets another operational risk: A GPU server presents PCIe link-width degradation immediately after a platform firmware change, but technicians plan to replace the accelerator first. The currently reported mechanism remains: Hardware-level errors may survive or precede OS logging and require out-of-band diagnostic evidence for root cause.

D:

This prevention control targets another operational risk: A GPU host is labeled fully functional based on a POST screen even though one DIMM records recurrent correctable ECC faults under memory load. The currently reported mechanism remains: Hardware-level errors may survive or precede OS logging and require out-of-band diagnostic evidence for root cause.

Question 73

An NVIDIA accelerator platform readiness review identifies this issue: An operator adds eight GPU servers to a rack because empty units are available, but a simultaneous training load trips the rack's PDU current limit. Which corrective deployment or operational action best addresses the stated problem?

  1. Validate thermal airflow and cooling capacity under representative sustained GPU utilization and correct hot-aisle containment or delivery gaps.
  2. Validate coolant flow, leak detection, and accelerator-specific inlet and thermal sensors under a sustained representative workload.
  3. Enroll and verify the TPM trust state and approved secure-boot measurement baseline before releasing the host into the training cluster.
  4. Perform rack-level peak power budgeting against PDU, branch circuit, UPS, and redundancy limits before installation.

Explanation

Correct Answer

D:

For the stated NVIDIA AI infrastructure condition (An operator adds eight GPU servers to a rack because empty units are available, but a simultaneous training load trips the rack's PDU current limit.), this action addresses the concrete failure: Perform rack-level peak power budgeting against PDU, branch circuit, UPS, and redundancy limits before installation. The underlying mechanism is: Available rack space does not guarantee sufficient peak electrical capacity for accelerator workloads. Verification would require: A full-load power test remains inside the approved derated electrical headroom with one designated power feed unavailable.

Incorrect Answers

A:

This option could be appropriate for a different NVIDIA infrastructure problem (1.5: Validate power and cooling parameters): A GPU cluster works at idle but repeatedly throttles during model training because the data center cooling model uses average rather than peak accelerator heat output. The present issue is more specifically caused by: Available rack space does not guarantee sufficient peak electrical capacity for accelerator workloads.

B:

This option could be appropriate for a different NVIDIA infrastructure problem (1.5: Validate power and cooling parameters): A liquid-cooled AI rack reports intermittent pump-flow warnings, but operations dismiss them because server CPU temperatures remain normal. The present issue is more specifically caused by: Available rack space does not guarantee sufficient peak electrical capacity for accelerator workloads.

C:

This option could be appropriate for a different NVIDIA infrastructure problem (1.3: Perform initial configuration of BMC, out-of-band management, and TPM): An AI appliance is deployed with measured boot enabled but platform ownership and TPM-backed trust measurements were never recorded. The present issue is more specifically caused by: Available rack space does not guarantee sufficient peak electrical capacity for accelerator workloads.

Question 74

An NVIDIA accelerator platform readiness review identifies this issue: An operator adds eight GPU servers to a rack because empty units are available, but a simultaneous training load trips the rack's PDU current limit. Which measured result most directly proves that this specific problem is resolved?

  1. A controlled removal of either designated supply path preserves protected host operation and a documented power alarm is raised.
  2. A full-load power test remains inside the approved derated electrical headroom with one designated power feed unavailable.
  3. Sustained load maintains inlet temperatures and clock behavior inside vendor operating thresholds throughout the warmest expected conditions.
  4. An audit of randomly selected servers reconstructs delivery batch, firmware baseline, cabling check, and final approval without ambiguity.

Explanation

Correct Answer

B:

This is the measurable acceptance result for the described case: A full-load power test remains inside the approved derated electrical headroom with one designated power feed unavailable. It validates the corrective operation (Perform rack-level peak power budgeting against PDU, branch circuit, UPS, and redundancy limits before installation.) rather than a related but different system check.

Incorrect Answers

A:

This observation would help verify a different incident: Dual PSU GPU nodes are wired to separate cords that both terminate on the same upstream electrical breaker, and a maintenance event shuts down the pod. It does not directly establish the required result for the current case: A full-load power test remains inside the approved derated electrical headroom with one designated power feed unavailable.

C:

This observation would help verify a different incident: A GPU cluster works at idle but repeatedly throttles during model training because the data center cooling model uses average rather than peak accelerator heat output. It does not directly establish the required result for the current case: A full-load power test remains inside the approved derated electrical headroom with one designated power feed unavailable.

D:

This observation would help verify a different incident: A supplier delivers AI servers in two waves, but operators mix acceptance logs and cannot tell which batch received the corrective firmware update. It does not directly establish the required result for the current case: A full-load power test remains inside the approved derated electrical headroom with one designated power feed unavailable.

Question 75

An NVIDIA accelerator platform readiness review identifies this issue: An operator adds eight GPU servers to a rack because empty units are available, but a simultaneous training load trips the rack's PDU current limit. Which root cause or infrastructure principle best explains the observed failure?

  1. CPU temperature can remain acceptable while liquid-cooling loss threatens densely packaged accelerators and interconnects.
  2. High-density AI servers create localized heat loads that aggregate room averages can conceal.
  3. Available rack space does not guarantee sufficient peak electrical capacity for accelerator workloads.
  4. A GPU can enumerate locally while its high-speed peer connections remain physically incomplete.

Explanation

Correct Answer

C:

This technical mechanism explains the reported condition: Available rack space does not guarantee sufficient peak electrical capacity for accelerator workloads. The observed situation is: An operator adds eight GPU servers to a rack because empty units are available, but a simultaneous training load trips the rack's PDU current limit. A requirement-aligned correction is: Perform rack-level peak power budgeting against PDU, branch circuit, UPS, and redundancy limits before installation.

Incorrect Answers

A:

This underlying cause is associated with another GPU platform incident: A liquid-cooled AI rack reports intermittent pump-flow warnings, but operations dismiss them because server CPU temperatures remain normal. The observed case instead concerns: Available rack space does not guarantee sufficient peak electrical capacity for accelerator workloads.

B:

This underlying cause is associated with another GPU platform incident: A GPU cluster works at idle but repeatedly throttles during model training because the data center cooling model uses average rather than peak accelerator heat output. The observed case instead concerns: Available rack space does not guarantee sufficient peak electrical capacity for accelerator workloads.

D:

This underlying cause is associated with another GPU platform incident: A GPU tray is installed after maintenance but one high-speed interconnect bridge remains loose, causing cross-GPU collective tests to fail. The observed case instead concerns: Available rack space does not guarantee sufficient peak electrical capacity for accelerator workloads.

Question 76

An NVIDIA accelerator platform readiness review identifies this issue: An operator adds eight GPU servers to a rack because empty units are available, but a simultaneous training load trips the rack's PDU current limit. Which repeatable operational safeguard best prevents recurrence of this failure?

  1. Retest cooling margins and rack power density after each significant accelerator population change.
  2. Require measured peak power and redundant-feed assumptions in every rack expansion request.
  3. Monitor rack cooling control parameters and exercise low-flow and leak responses as operating acceptance tests.
  4. Use staged firmware maintenance, image integrity checks, and verified recovery procedures for BMC updates.

Explanation

Correct Answer

B:

This preventive practice addresses the underlying failure (Available rack space does not guarantee sufficient peak electrical capacity for accelerator workloads.) and supports continued assurance through: A full-load power test remains inside the approved derated electrical headroom with one designated power feed unavailable.

Incorrect Answers

A:

This prevention control targets another operational risk: A GPU cluster works at idle but repeatedly throttles during model training because the data center cooling model uses average rather than peak accelerator heat output. The currently reported mechanism remains: Available rack space does not guarantee sufficient peak electrical capacity for accelerator workloads.

C:

This prevention control targets another operational risk: A liquid-cooled AI rack reports intermittent pump-flow warnings, but operations dismiss them because server CPU temperatures remain normal. The currently reported mechanism remains: Available rack space does not guarantee sufficient peak electrical capacity for accelerator workloads.

D:

This prevention control targets another operational risk: An operator initiates simultaneous BMC firmware updates on every host, eliminating the team's ability to recover remote power controls if the update fails. The currently reported mechanism remains: Available rack space does not guarantee sufficient peak electrical capacity for accelerator workloads.

Question 77

During a GPU datacenter maintenance window, an engineer discovers: A GPU cluster works at idle but repeatedly throttles during model training because the data center cooling model uses average rather than peak accelerator heat output. Which corrective deployment or operational action best addresses the stated problem?

  1. Verify independent electrical failure domains from PSU inputs back through PDU, panel, and supported upstream supply paths.
  2. Validate coolant flow, leak detection, and accelerator-specific inlet and thermal sensors under a sustained representative workload.
  3. Validate thermal airflow and cooling capacity under representative sustained GPU utilization and correct hot-aisle containment or delivery gaps.
  4. Compare physical slot and cable seating with PCIe enumeration and NVIDIA SMI device identifiers before running training.

Explanation

Correct Answer

C:

For the stated NVIDIA AI infrastructure condition (A GPU cluster works at idle but repeatedly throttles during model training because the data center cooling model uses average rather than peak accelerator heat output.), this action addresses the concrete failure: Validate thermal airflow and cooling capacity under representative sustained GPU utilization and correct hot-aisle containment or delivery gaps. The underlying mechanism is: High-density AI servers create localized heat loads that aggregate room averages can conceal. Verification would require: Sustained load maintains inlet temperatures and clock behavior inside vendor operating thresholds throughout the warmest expected conditions.

Incorrect Answers

A:

This option could be appropriate for a different NVIDIA infrastructure problem (1.5: Validate power and cooling parameters): Dual PSU GPU nodes are wired to separate cords that both terminate on the same upstream electrical breaker, and a maintenance event shuts down the pod. The present issue is more specifically caused by: High-density AI servers create localized heat loads that aggregate room averages can conceal.

B:

This option could be appropriate for a different NVIDIA infrastructure problem (1.5: Validate power and cooling parameters): A liquid-cooled AI rack reports intermittent pump-flow warnings, but operations dismiss them because server CPU temperatures remain normal. The present issue is more specifically caused by: High-density AI servers create localized heat loads that aggregate room averages can conceal.

D:

This option could be appropriate for a different NVIDIA infrastructure problem (1.6: Install GPU-based servers and use NVIDIA SMI): A server inventory says eight GPUs should be installed, but one accelerator intermittently disappears only after the chassis is moved between racks. The present issue is more specifically caused by: High-density AI servers create localized heat loads that aggregate room averages can conceal.

Question 78

During a GPU datacenter maintenance window, an engineer discovers: A GPU cluster works at idle but repeatedly throttles during model training because the data center cooling model uses average rather than peak accelerator heat output. Which measured result most directly proves that this specific problem is resolved?

  1. Sustained load maintains inlet temperatures and clock behavior inside vendor operating thresholds throughout the warmest expected conditions.
  2. A controlled removal of either designated supply path preserves protected host operation and a documented power alarm is raised.
  3. A full-load power test remains inside the approved derated electrical headroom with one designated power feed unavailable.
  4. A repeated failure correlates a device or thermal event across independent management logs and host telemetry with preserved timestamps.

Explanation

Correct Answer

A:

This is the measurable acceptance result for the described case: Sustained load maintains inlet temperatures and clock behavior inside vendor operating thresholds throughout the warmest expected conditions. It validates the corrective operation (Validate thermal airflow and cooling capacity under representative sustained GPU utilization and correct hot-aisle containment or delivery gaps.) rather than a related but different system check.

Incorrect Answers

B:

This observation would help verify a different incident: Dual PSU GPU nodes are wired to separate cords that both terminate on the same upstream electrical breaker, and a maintenance event shuts down the pod. It does not directly establish the required result for the current case: Sustained load maintains inlet temperatures and clock behavior inside vendor operating thresholds throughout the warmest expected conditions.

C:

This observation would help verify a different incident: An operator adds eight GPU servers to a rack because empty units are available, but a simultaneous training load trips the rack's PDU current limit. It does not directly establish the required result for the current case: Sustained load maintains inlet temperatures and clock behavior inside vendor operating thresholds throughout the warmest expected conditions.

D:

This observation would help verify a different incident: A node intermittently disappears during large CUDA workloads, but the only retained evidence is the operating-system kernel log after it has rebooted. It does not directly establish the required result for the current case: Sustained load maintains inlet temperatures and clock behavior inside vendor operating thresholds throughout the warmest expected conditions.

Question 79

During a GPU datacenter maintenance window, an engineer discovers: A GPU cluster works at idle but repeatedly throttles during model training because the data center cooling model uses average rather than peak accelerator heat output. Which root cause or infrastructure principle best explains the observed failure?

  1. CPU temperature can remain acceptable while liquid-cooling loss threatens densely packaged accelerators and interconnects.
  2. High-density AI servers create localized heat loads that aggregate room averages can conceal.
  3. Available rack space does not guarantee sufficient peak electrical capacity for accelerator workloads.
  4. Firmware and platform configuration can produce hardware-like symptoms that do not prove physical GPU failure.

Explanation

Correct Answer

B:

This technical mechanism explains the reported condition: High-density AI servers create localized heat loads that aggregate room averages can conceal. The observed situation is: A GPU cluster works at idle but repeatedly throttles during model training because the data center cooling model uses average rather than peak accelerator heat output. A requirement-aligned correction is: Validate thermal airflow and cooling capacity under representative sustained GPU utilization and correct hot-aisle containment or delivery gaps.

Incorrect Answers

A:

This underlying cause is associated with another GPU platform incident: A liquid-cooled AI rack reports intermittent pump-flow warnings, but operations dismiss them because server CPU temperatures remain normal. The observed case instead concerns: High-density AI servers create localized heat loads that aggregate room averages can conceal.

C:

This underlying cause is associated with another GPU platform incident: An operator adds eight GPU servers to a rack because empty units are available, but a simultaneous training load trips the rack's PDU current limit. The observed case instead concerns: High-density AI servers create localized heat loads that aggregate room averages can conceal.

D:

This underlying cause is associated with another GPU platform incident: A GPU server presents PCIe link-width degradation immediately after a platform firmware change, but technicians plan to replace the accelerator first. The observed case instead concerns: High-density AI servers create localized heat loads that aggregate room averages can conceal.

Question 80

During a GPU datacenter maintenance window, an engineer discovers: A GPU cluster works at idle but repeatedly throttles during model training because the data center cooling model uses average rather than peak accelerator heat output. Which repeatable operational safeguard best prevents recurrence of this failure?

  1. Require measured peak power and redundant-feed assumptions in every rack expansion request.
  2. Retest cooling margins and rack power density after each significant accelerator population change.
  3. Maintain end-to-end power path tracing and include breaker isolation in physical acceptance testing.
  4. Require transceiver/fiber compatibility and receive-power validation in the AI fabric cable acceptance checklist.

Explanation

Correct Answer

B:

This preventive practice addresses the underlying failure (High-density AI servers create localized heat loads that aggregate room averages can conceal.) and supports continued assurance through: Sustained load maintains inlet temperatures and clock behavior inside vendor operating thresholds throughout the warmest expected conditions.

Incorrect Answers

A:

This prevention control targets another operational risk: An operator adds eight GPU servers to a rack because empty units are available, but a simultaneous training load trips the rack's PDU current limit. The currently reported mechanism remains: High-density AI servers create localized heat loads that aggregate room averages can conceal.

C:

This prevention control targets another operational risk: Dual PSU GPU nodes are wired to separate cords that both terminate on the same upstream electrical breaker, and a maintenance event shuts down the pod. The currently reported mechanism remains: High-density AI servers create localized heat loads that aggregate room averages can conceal.

D:

This prevention control targets another operational risk: A GPU training fabric uses optics intended for short single-mode links on multimode building fiber and experiences frequent lane errors despite a link-up state. The currently reported mechanism remains: High-density AI servers create localized heat loads that aggregate room averages can conceal.

NCP-AI Infrastructure (NCP-AII) Practice Tests