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 16

Question 150

An infrastructure administrator receives the following incident: A GPU tray is installed after maintenance but one high-speed interconnect bridge remains loose, causing cross-GPU collective tests to fail. Which measured result most directly proves that this specific problem is resolved?

  1. The mounted card meets supported clearance and power limits and completes sustained temperature and fault tests without throttling.
  2. After reinstalling the supported replacement, the device appears under the correct UUID/slot and passes a sustained GPU validation run.
  3. PCIe topology and accelerator inventory match the expected slot map and the host passes a GPU-specific smoke test.
  4. Fabric diagnostics and a multi-GPU communication test show all intended topology links operating with acceptable errors.

Explanation

Correct Answer

D:

This is the measurable acceptance result for the described case: Fabric diagnostics and a multi-GPU communication test show all intended topology links operating with acceptable errors. It validates the corrective operation (Check the supported GPU and NVLink/NVSwitch interconnect assembly procedure and verify connector seating before enabling workloads.) rather than a related but different system check.

Incorrect Answers

A:

This observation would help verify a different incident: A rack expansion includes a GPU model that exceeds the chassis cooling and mechanical support envelope, despite fitting nominally into its PCIe slot. It does not directly establish the required result for the current case: Fabric diagnostics and a multi-GPU communication test show all intended topology links operating with acceptable errors.

B:

This observation would help verify a different incident: An engineer removes a failed GPU without using the vendor-defined ESD and power-discharge process, and the replacement later shows intermittent errors. It does not directly establish the required result for the current case: Fabric diagnostics and a multi-GPU communication test show all intended topology links operating with acceptable errors.

C:

This observation would help verify a different incident: A provisioning script accepts a data center node after disk health checks, but the BIOS is configured to hide required PCIe slots and GPUs are unavailable. It does not directly establish the required result for the current case: Fabric diagnostics and a multi-GPU communication test show all intended topology links operating with acceptable errors.

Question 151

An infrastructure administrator receives the following incident: A GPU tray is installed after maintenance but one high-speed interconnect bridge remains loose, causing cross-GPU collective tests to fail. Which root cause or infrastructure principle best explains the observed failure?

  1. Improper physical accelerator handling can introduce latent damage even when the initial host POST succeeds.
  2. PCIe slot compatibility alone does not certify power delivery, mechanical retention, or cooling for a high-power GPU.
  3. A successful power-on self-test can miss developing memory faults that only appear during sustained workload stress.
  4. A GPU can enumerate locally while its high-speed peer connections remain physically incomplete.

Explanation

Correct Answer

D:

This technical mechanism explains the reported condition: A GPU can enumerate locally while its high-speed peer connections remain physically incomplete. The observed situation is: A GPU tray is installed after maintenance but one high-speed interconnect bridge remains loose, causing cross-GPU collective tests to fail. A requirement-aligned correction is: Check the supported GPU and NVLink/NVSwitch interconnect assembly procedure and verify connector seating before enabling workloads.

Incorrect Answers

A:

This underlying cause is associated with another GPU platform incident: An engineer removes a failed GPU without using the vendor-defined ESD and power-discharge process, and the replacement later shows intermittent errors. The observed case instead concerns: A GPU can enumerate locally while its high-speed peer connections remain physically incomplete.

B:

This underlying cause is associated with another GPU platform incident: A rack expansion includes a GPU model that exceeds the chassis cooling and mechanical support envelope, despite fitting nominally into its PCIe slot. The observed case instead concerns: A GPU can enumerate locally while its high-speed peer connections remain physically incomplete.

C:

This underlying cause is associated with another GPU platform 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. The observed case instead concerns: A GPU can enumerate locally while its high-speed peer connections remain physically incomplete.

Question 152

An infrastructure administrator receives the following incident: A GPU tray is installed after maintenance but one high-speed interconnect bridge remains loose, causing cross-GPU collective tests to fail. Which repeatable operational safeguard best prevents recurrence of this failure?

  1. Require platform-qualified accelerator part lists and installation checks before approving physical hardware changes.
  2. Train field staff and document power isolation, ESD, and connector handling as mandatory GPU service steps.
  3. Retest cooling margins and rack power density after each significant accelerator population change.
  4. Require physical interconnect inspection and multi-GPU fabric verification after every tray or board service action.

Explanation

Correct Answer

D:

This preventive practice addresses the underlying failure (A GPU can enumerate locally while its high-speed peer connections remain physically incomplete.) and supports continued assurance through: Fabric diagnostics and a multi-GPU communication test show all intended topology links operating with acceptable errors.

Incorrect Answers

A:

This prevention control targets another operational risk: A rack expansion includes a GPU model that exceeds the chassis cooling and mechanical support envelope, despite fitting nominally into its PCIe slot. The currently reported mechanism remains: A GPU can enumerate locally while its high-speed peer connections remain physically incomplete.

B:

This prevention control targets another operational risk: An engineer removes a failed GPU without using the vendor-defined ESD and power-discharge process, and the replacement later shows intermittent errors. The currently reported mechanism remains: A GPU can enumerate locally while its high-speed peer connections remain physically incomplete.

C:

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: A GPU can enumerate locally while its high-speed peer connections remain physically incomplete.

Question 153

An NVIDIA accelerator platform readiness review identifies this issue: After drivers are installed, eight GPUs appear in the host inventory, but a test training program fails when allocating memory on one device. Which corrective deployment or operational action best addresses the stated problem?

  1. Validate GPU performance against the actual approved power, thermal, and job scheduler settings rather than a temporary unrestricted configuration.
  2. Validate multi-node GPU communication with NCCL under the intended fabric and test peer reachability and interface selection.
  3. Design a nonblocking or suitably engineered leaf-spine training fabric with measured bisection bandwidth and failure tolerance.
  4. Run a small GPU workload and device-memory stress test on each accelerator rather than accepting enumeration alone.

Explanation

Correct Answer

D:

For the stated NVIDIA AI infrastructure condition (After drivers are installed, eight GPUs appear in the host inventory, but a test training program fails when allocating memory on one device.), this action addresses the concrete failure: Run a small GPU workload and device-memory stress test on each accelerator rather than accepting enumeration alone. The underlying mechanism is: Driver enumeration validates visibility but not full memory or compute functionality under workload demand. Verification would require: Each GPU completes the representative allocation and compute check with no new Xid events or unacceptable ECC alarms.

Incorrect Answers

A:

This option could be appropriate for a different NVIDIA infrastructure problem (1.10: Validate hardware operation for workloads): A node passes an accelerator benchmark only when power caps are disabled, but the approved rack design requires a constrained energy profile. The present issue is more specifically caused by: Driver enumeration validates visibility but not full memory or compute functionality under workload demand.

B:

This option could be appropriate for a different NVIDIA infrastructure problem (1.10: Validate hardware operation for workloads): A single-node CUDA test passes, yet a distributed job hangs because the NVIDIA collective communication transport cannot communicate across hosts. The present issue is more specifically caused by: Driver enumeration validates visibility but not full memory or compute functionality under workload demand.

C:

This option could be appropriate for a different NVIDIA infrastructure problem (1.2: Describe network topologies for AI factories): A training farm connects every GPU node through one oversubscribed top-of-rack switch even though collective jobs exchange large volumes of east-west gradients. The present issue is more specifically caused by: Driver enumeration validates visibility but not full memory or compute functionality under workload demand.

Question 154

An NVIDIA accelerator platform readiness review identifies this issue: After drivers are installed, eight GPUs appear in the host inventory, but a test training program fails when allocating memory on one device. Which measured result most directly proves that this specific problem is resolved?

  1. A sustained model workload meets the defined service target within recorded rack power and device thermal bounds.
  2. Each GPU completes the representative allocation and compute check with no new Xid events or unacceptable ECC alarms.
  3. A sufficiently long repeatable workload completes without unexplained data errors, memory faults, or device-reset events.
  4. The staged bring-up record maps each GPU chassis to dual feeds and the controlled PDU outage leaves protected nodes powered.

Explanation

Correct Answer

B:

This is the measurable acceptance result for the described case: Each GPU completes the representative allocation and compute check with no new Xid events or unacceptable ECC alarms. It validates the corrective operation (Run a small GPU workload and device-memory stress test on each accelerator rather than accepting enumeration alone.) rather than a related but different system check.

Incorrect Answers

A:

This observation would help verify a different incident: A node passes an accelerator benchmark only when power caps are disabled, but the approved rack design requires a constrained energy profile. It does not directly establish the required result for the current case: Each GPU completes the representative allocation and compute check with no new Xid events or unacceptable ECC alarms.

C:

This observation would help verify a different incident: A new host completes a quick matrix multiplication test but produces intermittent errors on a long mixed-precision training workload with high HBM utilization. It does not directly establish the required result for the current case: Each GPU completes the representative allocation and compute check with no new Xid events or unacceptable ECC alarms.

D:

This observation would help verify a different incident: A new GPU pod is powered on before rack inventory and PDU mapping are approved, and engineers cannot identify which chassis will lose power during maintenance. It does not directly establish the required result for the current case: Each GPU completes the representative allocation and compute check with no new Xid events or unacceptable ECC alarms.

Question 155

An NVIDIA accelerator platform readiness review identifies this issue: After drivers are installed, eight GPUs appear in the host inventory, but a test training program fails when allocating memory on one device. Which root cause or infrastructure principle best explains the observed failure?

  1. Driver enumeration validates visibility but not full memory or compute functionality under workload demand.
  2. A test performed outside the production power envelope is not valid evidence of deployed workload readiness.
  3. Local GPU success does not demonstrate readiness of the network and software required for distributed collective training.
  4. Available rack space does not guarantee sufficient peak electrical capacity for accelerator workloads.

Explanation

Correct Answer

A:

This technical mechanism explains the reported condition: Driver enumeration validates visibility but not full memory or compute functionality under workload demand. The observed situation is: After drivers are installed, eight GPUs appear in the host inventory, but a test training program fails when allocating memory on one device. A requirement-aligned correction is: Run a small GPU workload and device-memory stress test on each accelerator rather than accepting enumeration alone.

Incorrect Answers

B:

This underlying cause is associated with another GPU platform incident: A node passes an accelerator benchmark only when power caps are disabled, but the approved rack design requires a constrained energy profile. The observed case instead concerns: Driver enumeration validates visibility but not full memory or compute functionality under workload demand.

C:

This underlying cause is associated with another GPU platform incident: A single-node CUDA test passes, yet a distributed job hangs because the NVIDIA collective communication transport cannot communicate across hosts. The observed case instead concerns: Driver enumeration validates visibility but not full memory or compute functionality under workload demand.

D:

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: Driver enumeration validates visibility but not full memory or compute functionality under workload demand.

Question 156

An NVIDIA accelerator platform readiness review identifies this issue: After drivers are installed, eight GPUs appear in the host inventory, but a test training program fails when allocating memory on one device. Which repeatable operational safeguard best prevents recurrence of this failure?

  1. Add GPU compute and memory smoke tests to the final workload-readiness acceptance phase.
  2. Standardize benchmark environment constraints and record power settings with acceptance results.
  3. Define representative GPU stress profiles and minimum runtime for each approved workload class.
  4. Use staged firmware maintenance, image integrity checks, and verified recovery procedures for BMC updates.

Explanation

Correct Answer

A:

This preventive practice addresses the underlying failure (Driver enumeration validates visibility but not full memory or compute functionality under workload demand.) and supports continued assurance through: Each GPU completes the representative allocation and compute check with no new Xid events or unacceptable ECC alarms.

Incorrect Answers

B:

This prevention control targets another operational risk: A node passes an accelerator benchmark only when power caps are disabled, but the approved rack design requires a constrained energy profile. The currently reported mechanism remains: Driver enumeration validates visibility but not full memory or compute functionality under workload demand.

C:

This prevention control targets another operational risk: A new host completes a quick matrix multiplication test but produces intermittent errors on a long mixed-precision training workload with high HBM utilization. The currently reported mechanism remains: Driver enumeration validates visibility but not full memory or compute functionality under workload demand.

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: Driver enumeration validates visibility but not full memory or compute functionality under workload demand.

Question 157

During a GPU datacenter maintenance window, an engineer discovers: A single-node CUDA test passes, yet a distributed job hangs because the NVIDIA collective communication transport cannot communicate across hosts. Which corrective deployment or operational action best addresses the stated problem?

  1. Extend workload validation to representative precision modes, memory capacity, and sustained run duration while collecting GPU health metrics.
  2. Validate multi-node GPU communication with NCCL under the intended fabric and test peer reachability and interface selection.
  3. Run a small GPU workload and device-memory stress test on each accelerator rather than accepting enumeration alone.
  4. Configure unique BMC credentials, isolated out-of-band addressing, role-limited access, and an audited first-login process.

Explanation

Correct Answer

B:

For the stated NVIDIA AI infrastructure condition (A single-node CUDA test passes, yet a distributed job hangs because the NVIDIA collective communication transport cannot communicate across hosts.), this action addresses the concrete failure: Validate multi-node GPU communication with NCCL under the intended fabric and test peer reachability and interface selection. The underlying mechanism is: Local GPU success does not demonstrate readiness of the network and software required for distributed collective training. Verification would require: A supported multi-node all-reduce exercise completes at the required bandwidth while link and device logs stay clean.

Incorrect Answers

A:

This option could be appropriate for a different NVIDIA infrastructure problem (1.10: Validate hardware operation for workloads): A new host completes a quick matrix multiplication test but produces intermittent errors on a long mixed-precision training workload with high HBM utilization. The present issue is more specifically caused by: Local GPU success does not demonstrate readiness of the network and software required for distributed collective training.

C:

This option could be appropriate for a different NVIDIA infrastructure problem (1.10: Validate hardware operation for workloads): After drivers are installed, eight GPUs appear in the host inventory, but a test training program fails when allocating memory on one device. The present issue is more specifically caused by: Local GPU success does not demonstrate readiness of the network and software required for distributed collective training.

D:

This option could be appropriate for a different NVIDIA infrastructure problem (1.3: Perform initial configuration of BMC, out-of-band management, and TPM): A newly received DGX-class server has default BMC credentials and its management interface is reachable from the general campus network. The present issue is more specifically caused by: Local GPU success does not demonstrate readiness of the network and software required for distributed collective training.

Question 158

During a GPU datacenter maintenance window, an engineer discovers: A single-node CUDA test passes, yet a distributed job hangs because the NVIDIA collective communication transport cannot communicate across hosts. Which measured result most directly proves that this specific problem is resolved?

  1. A sufficiently long repeatable workload completes without unexplained data errors, memory faults, or device-reset events.
  2. A supported multi-node all-reduce exercise completes at the required bandwidth while link and device logs stay clean.
  3. Each GPU completes the representative allocation and compute check with no new Xid events or unacceptable ECC alarms.
  4. A representative all-reduce run across distant racks reaches the approved throughput and remains operational after one specified uplink failure.

Explanation

Correct Answer

B:

This is the measurable acceptance result for the described case: A supported multi-node all-reduce exercise completes at the required bandwidth while link and device logs stay clean. It validates the corrective operation (Validate multi-node GPU communication with NCCL under the intended fabric and test peer reachability and interface selection.) rather than a related but different system check.

Incorrect Answers

A:

This observation would help verify a different incident: A new host completes a quick matrix multiplication test but produces intermittent errors on a long mixed-precision training workload with high HBM utilization. It does not directly establish the required result for the current case: A supported multi-node all-reduce exercise completes at the required bandwidth while link and device logs stay clean.

C:

This observation would help verify a different incident: After drivers are installed, eight GPUs appear in the host inventory, but a test training program fails when allocating memory on one device. It does not directly establish the required result for the current case: A supported multi-node all-reduce exercise completes at the required bandwidth while link and device logs stay clean.

D:

This observation would help verify a different incident: A training farm connects every GPU node through one oversubscribed top-of-rack switch even though collective jobs exchange large volumes of east-west gradients. It does not directly establish the required result for the current case: A supported multi-node all-reduce exercise completes at the required bandwidth while link and device logs stay clean.

Question 159

During a GPU datacenter maintenance window, an engineer discovers: A single-node CUDA test passes, yet a distributed job hangs because the NVIDIA collective communication transport cannot communicate across hosts. Which root cause or infrastructure principle best explains the observed failure?

  1. A test performed outside the production power envelope is not valid evidence of deployed workload readiness.
  2. Driver enumeration validates visibility but not full memory or compute functionality under workload demand.
  3. Local GPU success does not demonstrate readiness of the network and software required for distributed collective training.
  4. Unversioned deployment records obscure whether defects have been remediated on all affected physical assets.

Explanation

Correct Answer

C:

This technical mechanism explains the reported condition: Local GPU success does not demonstrate readiness of the network and software required for distributed collective training. The observed situation is: A single-node CUDA test passes, yet a distributed job hangs because the NVIDIA collective communication transport cannot communicate across hosts. A requirement-aligned correction is: Validate multi-node GPU communication with NCCL under the intended fabric and test peer reachability and interface selection.

Incorrect Answers

A:

This underlying cause is associated with another GPU platform incident: A node passes an accelerator benchmark only when power caps are disabled, but the approved rack design requires a constrained energy profile. The observed case instead concerns: Local GPU success does not demonstrate readiness of the network and software required for distributed collective training.

B:

This underlying cause is associated with another GPU platform incident: After drivers are installed, eight GPUs appear in the host inventory, but a test training program fails when allocating memory on one device. The observed case instead concerns: Local GPU success does not demonstrate readiness of the network and software required for distributed collective training.

D:

This underlying cause is associated with another GPU platform incident: A supplier delivers AI servers in two waves, but operators mix acceptance logs and cannot tell which batch received the corrective firmware update. The observed case instead concerns: Local GPU success does not demonstrate readiness of the network and software required for distributed collective training.

Question 160

During a GPU datacenter maintenance window, an engineer discovers: A single-node CUDA test passes, yet a distributed job hangs because the NVIDIA collective communication transport cannot communicate across hosts. Which repeatable operational safeguard best prevents recurrence of this failure?

  1. Standardize benchmark environment constraints and record power settings with acceptance results.
  2. Include inter-node collective workload checks before admitting GPU servers to a production training queue.
  3. Add GPU compute and memory smoke tests to the final workload-readiness acceptance phase.
  4. Validate part compatibility and power fault resilience during serialized hardware inventory acceptance.

Explanation

Correct Answer

B:

This preventive practice addresses the underlying failure (Local GPU success does not demonstrate readiness of the network and software required for distributed collective training.) and supports continued assurance through: A supported multi-node all-reduce exercise completes at the required bandwidth while link and device logs stay clean.

Incorrect Answers

A:

This prevention control targets another operational risk: A node passes an accelerator benchmark only when power caps are disabled, but the approved rack design requires a constrained energy profile. The currently reported mechanism remains: Local GPU success does not demonstrate readiness of the network and software required for distributed collective training.

C:

This prevention control targets another operational risk: After drivers are installed, eight GPUs appear in the host inventory, but a test training program fails when allocating memory on one device. The currently reported mechanism remains: Local GPU success does not demonstrate readiness of the network and software required for distributed collective training.

D:

This prevention control targets another operational risk: A recently delivered accelerator system has inconsistent PSU models across sibling nodes, and unexpected power alarms occur only during high utilization. The currently reported mechanism remains: Local GPU success does not demonstrate readiness of the network and software required for distributed collective training.

NCP-AI Infrastructure (NCP-AII) Practice Tests