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NVIDIA NCP-AII Practice Test 82

Question 810

During an NVIDIA AI factory acceptance test, engineers discover: An application maps two MPI ranks to the same GPU unexpectedly and overcommits accelerator memory. Which TWO complementary elements are needed: the correct technical action and the strongest independent diagnostic evidence? Select TWO answers.

  1. Validate the complete rank count, GPU mapping and launch environment on all participating nodes.
  2. Inspect process lists, world-size and rank variables, GPU device assignments and NCCL initialization logs.
  3. Run repeatable multi-node NCCL stress over representative durations, message sizes and full-rack participation.
  4. Ignore mismatched WORLD_SIZE and device maps because every worker independently reports available CUDA devices.
  5. Inspect nccl-tests latency, algorithmic and bus bandwidth, iteration variance and message-size curves.

Explanation

Correct Answers

A:

This is an appropriate correction for NCCL communicator and rank-to-device consistency: Validate the complete rank count, GPU mapping and launch environment on all participating nodes. Observed scenario: An application maps two MPI ranks to the same GPU unexpectedly and overcommits accelerator memory. The governing mechanism is: Collectives require coordinated communicator membership and consistent rank participation; partial launches can hang.

B:

This is the direct operational evidence to investigate: Inspect process lists, world-size and rank variables, GPU device assignments and NCCL initialization logs. Observed scenario: An application maps two MPI ranks to the same GPU unexpectedly and overcommits accelerator memory.

Incorrect Answers

C:

This alternative is relevant to Multi-node NCCL repeated burn-in but does not directly resolve the current NCCL communicator and rank-to-device consistency issue: Run repeatable multi-node NCCL stress over representative durations, message sizes and full-rack participation. Observed scenario: An application maps two MPI ranks to the same GPU unexpectedly and overcommits accelerator memory. The governing mechanism is: Collectives require coordinated communicator membership and consistent rank participation; partial launches can hang.

D:

This tempting but unsafe shortcut is not a sufficient remediation for NCCL communicator and rank-to-device consistency: Ignore mismatched WORLD_SIZE and device maps because every worker independently reports available CUDA devices. An application maps two MPI ranks to the same GPU unexpectedly and overcommits accelerator memory. Collectives require coordinated communicator membership and consistent rank participation; partial launches can hang.

E:

That option concerns NCCL bandwidth and latency payload sweep. Collectives require coordinated communicator membership and consistent rank participation; partial launches can hang.

Question 811

A production GPU cluster review identifies this condition: An application maps two MPI ranks to the same GPU unexpectedly and overcommits accelerator memory. Which diagnostic evidence should be obtained FIRST to substantiate the likely fault or configuration gap? Select ONE best answer.

  1. Ignore mismatched WORLD_SIZE and device maps because every worker independently reports available CUDA devices.
  2. Inspect deviceQuery or equivalent CUDA sample output, driver-runtime versions and kernel launch error codes.
  3. Inspect process lists, world-size and rank variables, GPU device assignments and NCCL initialization logs.
  4. Inspect NCCL debug logs, topology files where used, algorithm settings, payload sweep and link utilization.

Explanation

Correct Answer

C:

This is the direct operational evidence to investigate: Inspect process lists, world-size and rank variables, GPU device assignments and NCCL initialization logs. Observed scenario: An application maps two MPI ranks to the same GPU unexpectedly and overcommits accelerator memory. The governing mechanism is: Collectives require coordinated communicator membership and consistent rank participation; partial launches can hang.

Incorrect Answers

A:

This tempting but unsafe shortcut is not a sufficient remediation for NCCL communicator and rank-to-device consistency: Ignore mismatched WORLD_SIZE and device maps because every worker independently reports available CUDA devices. Observed scenario: An application maps two MPI ranks to the same GPU unexpectedly and overcommits accelerator memory. The governing mechanism is: Collectives require coordinated communicator membership and consistent rank participation; partial launches can hang. The proportionate action is: Validate the complete rank count, GPU mapping and launch environment on all participating nodes.

B:

This alternative is relevant to CUDA device query and kernel smoke tests but does not directly resolve the current NCCL communicator and rank-to-device consistency issue: Inspect deviceQuery or equivalent CUDA sample output, driver-runtime versions and kernel launch error codes. Observed scenario: An application maps two MPI ranks to the same GPU unexpectedly and overcommits accelerator memory.

D:

This alternative is relevant to NCCL algorithm choice and topology mapping but does not directly resolve the current NCCL communicator and rank-to-device consistency issue: Inspect NCCL debug logs, topology files where used, algorithm settings, payload sweep and link utilization. Observed scenario: An application maps two MPI ranks to the same GPU unexpectedly and overcommits accelerator memory.

Question 812

In a professional AI infrastructure deployment, a technician observes: An application maps two MPI ranks to the same GPU unexpectedly and overcommits accelerator memory. After corrective work, which observable result BEST confirms that the requirement is met? Select ONE best answer.

  1. Ignore mismatched WORLD_SIZE and device maps because every worker independently reports available CUDA devices.
  2. Each intended GPU successfully initializes and executes a minimal repeatable CUDA workload under its supported stack.
  3. All planned ranks join the intended communicator and finish the same collective without duplicate device placement.
  4. Configured algorithms meet the intended latency and throughput envelope across representative job sizes.

Explanation

Correct Answer

C:

This is an observable verified result, not merely a claimed configuration change: All planned ranks join the intended communicator and finish the same collective without duplicate device placement. Observed scenario: An application maps two MPI ranks to the same GPU unexpectedly and overcommits accelerator memory.

Incorrect Answers

A:

This tempting but unsafe shortcut is not a sufficient remediation for NCCL communicator and rank-to-device consistency: Ignore mismatched WORLD_SIZE and device maps because every worker independently reports available CUDA devices. Observed scenario: An application maps two MPI ranks to the same GPU unexpectedly and overcommits accelerator memory. The governing mechanism is: Collectives require coordinated communicator membership and consistent rank participation; partial launches can hang. The proportionate action is: Validate the complete rank count, GPU mapping and launch environment on all participating nodes. A direct check is: Inspect process lists, world-size and rank variables, GPU device assignments and NCCL initialization logs. The acceptance condition is: All planned ranks join the intended communicator and finish the same collective without duplicate device placement.

B:

This alternative is relevant to CUDA device query and kernel smoke tests but does not directly resolve the current NCCL communicator and rank-to-device consistency issue: Each intended GPU successfully initializes and executes a minimal repeatable CUDA workload under its supported stack. Observed scenario: An application maps two MPI ranks to the same GPU unexpectedly and overcommits accelerator memory.

D:

This alternative is relevant to NCCL algorithm choice and topology mapping but does not directly resolve the current NCCL communicator and rank-to-device consistency issue: Configured algorithms meet the intended latency and throughput envelope across representative job sizes. Observed scenario: An application maps two MPI ranks to the same GPU unexpectedly and overcommits accelerator memory.

Question 813

The cluster operations team needs to evaluate this situation: A distributed test's environment variables identify incompatible device selections between racks. Which operational response is MOST appropriate to address the root issue? Select ONE best answer.

  1. Validate the complete rank count, GPU mapping and launch environment on all participating nodes.
  2. Run supported single-node NCCL collectives spanning the intended local GPUs and compare with baseline topology.
  3. Run repeatable multi-node NCCL stress over representative durations, message sizes and full-rack participation.
  4. Ignore mismatched WORLD_SIZE and device maps because every worker independently reports available CUDA devices.

Explanation

Correct Answer

A:

This is an appropriate correction for NCCL communicator and rank-to-device consistency: Validate the complete rank count, GPU mapping and launch environment on all participating nodes. Observed scenario: A distributed test's environment variables identify incompatible device selections between racks.

Incorrect Answers

B:

This alternative is relevant to Intra-node NCCL all-reduce test scope but does not directly resolve the current NCCL communicator and rank-to-device consistency issue: Run supported single-node NCCL collectives spanning the intended local GPUs and compare with baseline topology. Observed scenario: A distributed test's environment variables identify incompatible device selections between racks.

C:

This alternative is relevant to Multi-node NCCL repeated burn-in but does not directly resolve the current NCCL communicator and rank-to-device consistency issue: Run repeatable multi-node NCCL stress over representative durations, message sizes and full-rack participation. Observed scenario: A distributed test's environment variables identify incompatible device selections between racks.

D:

This tempting but unsafe shortcut is not a sufficient remediation for NCCL communicator and rank-to-device consistency: Ignore mismatched WORLD_SIZE and device maps because every worker independently reports available CUDA devices. A distributed test's environment variables identify incompatible device selections between racks.

Question 814

At a multi-node AI infrastructure handoff, the team records: A distributed test's environment variables identify incompatible device selections between racks. Which underlying infrastructure principle BEST explains the issue? Select ONE best answer.

  1. Ignore mismatched WORLD_SIZE and device maps because every worker independently reports available CUDA devices.
  2. NCCL collective execution stresses GPU peer paths and coordination beyond the capabilities of independent CUDA kernels.
  3. A representative performance benchmark is meaningful only when the tested hardware and software baseline are documented.
  4. Collectives require coordinated communicator membership and consistent rank participation; partial launches can hang.

Explanation

Correct Answer

D:

This is the governing infrastructure principle: Collectives require coordinated communicator membership and consistent rank participation; partial launches can hang. Observed scenario: A distributed test's environment variables identify incompatible device selections between racks.

Incorrect Answers

A:

This tempting but unsafe shortcut is not a sufficient remediation for NCCL communicator and rank-to-device consistency: Ignore mismatched WORLD_SIZE and device maps because every worker independently reports available CUDA devices. A distributed test's environment variables identify incompatible device selections between racks. Collectives require coordinated communicator membership and consistent rank participation; partial launches can hang.

B:

This alternative is relevant to Intra-node NCCL all-reduce test scope but does not directly resolve the current NCCL communicator and rank-to-device consistency issue: NCCL collective execution stresses GPU peer paths and coordination beyond the capabilities of independent CUDA kernels. Observed scenario: A distributed test's environment variables identify incompatible device selections between racks.

C:

That option concerns Cluster-wide preflight hardware inventory. The acceptance condition is: All planned ranks join the intended communicator and finish the same collective without duplicate device placement.

Question 815

During an NVIDIA AI factory acceptance test, engineers discover: A distributed test's environment variables identify incompatible device selections between racks. Which diagnostic evidence should be obtained FIRST to substantiate the likely fault or configuration gap? Select ONE best answer.

  1. Ignore mismatched WORLD_SIZE and device maps because every worker independently reports available CUDA devices.
  2. Inspect NCCL debug traces, selected network plugin, environment variables and host network topology.
  3. Inspect process lists, world-size and rank variables, GPU device assignments and NCCL initialization logs.
  4. Inspect deviceQuery or equivalent CUDA sample output, driver-runtime versions and kernel launch error codes.

Explanation

Correct Answer

C:

This is the direct operational evidence to investigate: Inspect process lists, world-size and rank variables, GPU device assignments and NCCL initialization logs. Observed scenario: A distributed test's environment variables identify incompatible device selections between racks.

Incorrect Answers

A:

This tempting but unsafe shortcut is not a sufficient remediation for NCCL communicator and rank-to-device consistency: Ignore mismatched WORLD_SIZE and device maps because every worker independently reports available CUDA devices. Observed scenario: A distributed test's environment variables identify incompatible device selections between racks. The governing mechanism is: Collectives require coordinated communicator membership and consistent rank participation; partial launches can hang. The proportionate action is: Validate the complete rank count, GPU mapping and launch environment on all participating nodes.

B:

This alternative is relevant to NCCL transport environment and benchmark reproducibility but does not directly resolve the current NCCL communicator and rank-to-device consistency issue: Inspect NCCL debug traces, selected network plugin, environment variables and host network topology. Observed scenario: A distributed test's environment variables identify incompatible device selections between racks.

D:

This alternative is relevant to CUDA device query and kernel smoke tests but does not directly resolve the current NCCL communicator and rank-to-device consistency issue: Inspect deviceQuery or equivalent CUDA sample output, driver-runtime versions and kernel launch error codes. Observed scenario: A distributed test's environment variables identify incompatible device selections between racks.

Question 816

A production GPU cluster review identifies this condition: A distributed test's environment variables identify incompatible device selections between racks. After corrective work, which observable result BEST confirms that the requirement is met? Select ONE best answer.

  1. All planned ranks join the intended communicator and finish the same collective without duplicate device placement.
  2. Ignore mismatched WORLD_SIZE and device maps because every worker independently reports available CUDA devices.
  3. Each intended GPU successfully initializes and executes a minimal repeatable CUDA workload under its supported stack.
  4. The collective returns numerically valid outputs across intended ranks at accepted latency and bandwidth.

Explanation

Correct Answer

A:

This is an observable verified result, not merely a claimed configuration change: All planned ranks join the intended communicator and finish the same collective without duplicate device placement. Observed scenario: A distributed test's environment variables identify incompatible device selections between racks.

Incorrect Answers

B:

This tempting but unsafe shortcut is not a sufficient remediation for NCCL communicator and rank-to-device consistency: Ignore mismatched WORLD_SIZE and device maps because every worker independently reports available CUDA devices. Observed scenario: A distributed test's environment variables identify incompatible device selections between racks. The governing mechanism is: Collectives require coordinated communicator membership and consistent rank participation; partial launches can hang. The proportionate action is: Validate the complete rank count, GPU mapping and launch environment on all participating nodes. A direct check is: Inspect process lists, world-size and rank variables, GPU device assignments and NCCL initialization logs. The acceptance condition is: All planned ranks join the intended communicator and finish the same collective without duplicate device placement.

C:

This alternative is relevant to CUDA device query and kernel smoke tests but does not directly resolve the current NCCL communicator and rank-to-device consistency issue: Each intended GPU successfully initializes and executes a minimal repeatable CUDA workload under its supported stack. Observed scenario: A distributed test's environment variables identify incompatible device selections between racks.

D:

This alternative is relevant to NCCL device buffers and data integrity checks but does not directly resolve the current NCCL communicator and rank-to-device consistency issue: The collective returns numerically valid outputs across intended ranks at accepted latency and bandwidth. Observed scenario: A distributed test's environment variables identify incompatible device selections between racks.

Question 817

In a professional AI infrastructure deployment, a technician observes: A tuning script forces socket transport after a previous RDMA outage and measured NCCL bandwidth remains low. Which operational response is MOST appropriate to address the root issue? Select ONE best answer.

  1. Treat environment-driven NCCL transport differences as physical fabric faults without examining launch settings.
  2. Schedule supported active DCGM diagnostics for the relevant GPU class and investigate detailed failures.
  3. Validate collective correctness over representative sizes and iteration counts alongside throughput.
  4. Capture and rationalize NCCL environment settings and selected interfaces with each repeatable benchmark.

Explanation

Correct Answer

D:

This is an appropriate correction for NCCL transport environment and benchmark reproducibility: Capture and rationalize NCCL environment settings and selected interfaces with each repeatable benchmark. Observed scenario: A tuning script forces socket transport after a previous RDMA outage and measured NCCL bandwidth remains low.

Incorrect Answers

A:

This tempting but unsafe shortcut is not a sufficient remediation for NCCL transport environment and benchmark reproducibility: Treat environment-driven NCCL transport differences as physical fabric faults without examining launch settings.

B:

This alternative is relevant to DCGM active single-node diagnostic suites but does not directly resolve the current NCCL transport environment and benchmark reproducibility issue: Schedule supported active DCGM diagnostics for the relevant GPU class and investigate detailed failures. Observed scenario: A tuning script forces socket transport after a previous RDMA outage and measured NCCL bandwidth remains low.

C:

This alternative is relevant to NCCL device buffers and data integrity checks but does not directly resolve the current NCCL transport environment and benchmark reproducibility issue: Validate collective correctness over representative sizes and iteration counts alongside throughput. Observed scenario: A tuning script forces socket transport after a previous RDMA outage and measured NCCL bandwidth remains low.

Question 818

The cluster operations team needs to evaluate this situation: A tuning script forces socket transport after a previous RDMA outage and measured NCCL bandwidth remains low. Which underlying infrastructure principle BEST explains the issue? Select ONE best answer.

  1. Environment overrides can change transport or algorithm choice; performance claims require documented configuration.
  2. A representative performance benchmark is meaningful only when the tested hardware and software baseline are documented.
  3. Network and collective performance vary by size, concurrency and serialization overhead; one peak number is incomplete.
  4. Treat environment-driven NCCL transport differences as physical fabric faults without examining launch settings.

Explanation

Correct Answer

A:

This is the governing infrastructure principle: Environment overrides can change transport or algorithm choice; performance claims require documented configuration. Observed scenario: A tuning script forces socket transport after a previous RDMA outage and measured NCCL bandwidth remains low.

Incorrect Answers

B:

That option concerns Cluster-wide preflight hardware inventory. The acceptance condition is: Benchmark runs can be repeated with the same approved transport and explainable performance changes.

C:

That option concerns NCCL bandwidth and latency payload sweep. The acceptance condition is: Benchmark runs can be repeated with the same approved transport and explainable performance changes.

D:

This tempting but unsafe shortcut is not a sufficient remediation for NCCL transport environment and benchmark reproducibility: Treat environment-driven NCCL transport differences as physical fabric faults without examining launch settings. Environment overrides can change transport or algorithm choice; performance claims require documented configuration.

Question 819

At a multi-node AI infrastructure handoff, the team records: A tuning script forces socket transport after a previous RDMA outage and measured NCCL bandwidth remains low. Which diagnostic evidence should be obtained FIRST to substantiate the likely fault or configuration gap? Select ONE best answer.

  1. Inspect dcgmi diag suite results, entity IDs, error logs, driver state and conflicting running workloads.
  2. Inspect NCCL debug traces, selected network plugin, environment variables and host network topology.
  3. Inspect node inventory, GPU and NIC UUIDs, firmware and driver versions, cabling records and test eligibility.
  4. Treat environment-driven NCCL transport differences as physical fabric faults without examining launch settings.

Explanation

Correct Answer

B:

This is the direct operational evidence to investigate: Inspect NCCL debug traces, selected network plugin, environment variables and host network topology. Observed scenario: A tuning script forces socket transport after a previous RDMA outage and measured NCCL bandwidth remains low.

Incorrect Answers

A:

This alternative is relevant to DCGM active single-node diagnostic suites but does not directly resolve the current NCCL transport environment and benchmark reproducibility issue: Inspect dcgmi diag suite results, entity IDs, error logs, driver state and conflicting running workloads. Observed scenario: A tuning script forces socket transport after a previous RDMA outage and measured NCCL bandwidth remains low.

C:

This alternative is relevant to Cluster-wide preflight hardware inventory but does not directly resolve the current NCCL transport environment and benchmark reproducibility issue: Inspect node inventory, GPU and NIC UUIDs, firmware and driver versions, cabling records and test eligibility. Observed scenario: A tuning script forces socket transport after a previous RDMA outage and measured NCCL bandwidth remains low.

D:

This tempting but unsafe shortcut is not a sufficient remediation for NCCL transport environment and benchmark reproducibility: Treat environment-driven NCCL transport differences as physical fabric faults without examining launch settings. A tuning script forces socket transport after a previous RDMA outage and measured NCCL bandwidth remains low.

Question 820

During an NVIDIA AI factory acceptance test, engineers discover: A tuning script forces socket transport after a previous RDMA outage and measured NCCL bandwidth remains low. Which TWO complementary elements are needed: the correct technical action and the strongest independent diagnostic evidence? Select TWO answers.

  1. Capture and rationalize NCCL environment settings and selected interfaces with each repeatable benchmark.
  2. Test NCCL throughput and latency across representative message sizes and node counts with repeatable conditions.
  3. Inspect RDMA device state, NCCL transport logs, port counters, rank mapping and cross-node bandwidth.
  4. Treat environment-driven NCCL transport differences as physical fabric faults without examining launch settings.
  5. Inspect NCCL debug traces, selected network plugin, environment variables and host network topology.

Explanation

Correct Answers

A:

This is an appropriate correction for NCCL transport environment and benchmark reproducibility: Capture and rationalize NCCL environment settings and selected interfaces with each repeatable benchmark. Observed scenario: A tuning script forces socket transport after a previous RDMA outage and measured NCCL bandwidth remains low. The governing mechanism is: Environment overrides can change transport or algorithm choice; performance claims require documented configuration.

E:

This is the direct operational evidence to investigate: Inspect NCCL debug traces, selected network plugin, environment variables and host network topology. Observed scenario: A tuning script forces socket transport after a previous RDMA outage and measured NCCL bandwidth remains low. The governing mechanism is: Environment overrides can change transport or algorithm choice; performance claims require documented configuration.

Incorrect Answers

B:

That option concerns NCCL bandwidth and latency payload sweep. Environment overrides can change transport or algorithm choice; performance claims require documented configuration.

C:

This alternative is relevant to Inter-node NCCL east-west RDMA readiness but does not directly resolve the current NCCL transport environment and benchmark reproducibility issue: Inspect RDMA device state, NCCL transport logs, port counters, rank mapping and cross-node bandwidth. Observed scenario: A tuning script forces socket transport after a previous RDMA outage and measured NCCL bandwidth remains low.

D:

This tempting but unsafe shortcut is not a sufficient remediation for NCCL transport environment and benchmark reproducibility: Treat environment-driven NCCL transport differences as physical fabric faults without examining launch settings. A tuning script forces socket transport after a previous RDMA outage and measured NCCL bandwidth remains low. Environment overrides can change transport or algorithm choice; performance claims require documented configuration.

NCP-AI Infrastructure (NCP-AII) Practice Tests