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Exam Code: NCP-AII

Exam Name: NCP-AI Infrastructure

Certification Provider: NVIDIA

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NVIDIA NCP-AII Practice Test 30

Question 290

An infrastructure administrator receives the following incident: A Docker AI image launches on a GPU host but reports no CUDA devices because the NVIDIA container runtime integration was never installed. Which measured result most directly proves that this specific problem is resolved?

  1. A small container runs the approved `nvidia-smi` or CUDA probe and reports the allocated device UUIDs correctly.
  2. A negative tenant test cannot enumerate unauthorized accelerators while the permitted device runs the expected workload.
  3. The approved container resolves matching CUDA driver interfaces and completes a representative GPU task after the upgrade.
  4. A deliberately interrupted test job frees its GPU assignments and leaves the host ready for a new tenant after verified cleanup.

Explanation

Correct Answer

A:

This is the measurable acceptance result for the described case: A small container runs the approved `nvidia-smi` or CUDA probe and reports the allocated device UUIDs correctly. It validates the corrective operation (Install and configure the NVIDIA Container Toolkit for the supported container runtime and test GPU device injection.) rather than a related but different system check.

Incorrect Answers

B:

This observation would help verify a different incident: A container in a multi-tenant GPU cluster can see accelerators allocated to another job because the runtime launches with unrestricted device exposure. It does not directly establish the required result for the current case: A small container runs the approved `nvidia-smi` or CUDA probe and reports the allocated device UUIDs correctly.

C:

This observation would help verify a different incident: After a driver upgrade, the container toolkit continues referencing stale GPU library paths, and a container loads incompatible runtime libraries. It does not directly establish the required result for the current case: A small container runs the approved `nvidia-smi` or CUDA probe and reports the allocated device UUIDs correctly.

D:

This observation would help verify a different incident: Slurm reports some nodes as allocated indefinitely after a failed distributed job because job cleanup leaves Enroot container artifacts and stale GPU processes. It does not directly establish the required result for the current case: A small container runs the approved `nvidia-smi` or CUDA probe and reports the allocated device UUIDs correctly.

Question 291

An infrastructure administrator receives the following incident: A Docker AI image launches on a GPU host but reports no CUDA devices because the NVIDIA container runtime integration was never installed. Which root cause or infrastructure principle best explains the observed failure?

  1. Inconsistent container-runtime GPU configuration causes workload failures independent of GPU hardware health.
  2. An installed host driver does not automatically expose accelerators inside isolated containers.
  3. Containerized GPU applications depend on correct host-driver library injection as well as packaged userspace software.
  4. Stale workloads and container state can make a healthy node appear occupied or leak resource ownership between jobs.

Explanation

Correct Answer

B:

This technical mechanism explains the reported condition: An installed host driver does not automatically expose accelerators inside isolated containers. The observed situation is: A Docker AI image launches on a GPU host but reports no CUDA devices because the NVIDIA container runtime integration was never installed. A requirement-aligned correction is: Install and configure the NVIDIA Container Toolkit for the supported container runtime and test GPU device injection.

Incorrect Answers

A:

This underlying cause is associated with another GPU platform incident: An operations team edits container runtime configuration by hand on some nodes, and CUDA containers work on one rack but fail after rescheduling to another rack. The observed case instead concerns: An installed host driver does not automatically expose accelerators inside isolated containers.

C:

This underlying cause is associated with another GPU platform incident: After a driver upgrade, the container toolkit continues referencing stale GPU library paths, and a container loads incompatible runtime libraries. The observed case instead concerns: An installed host driver does not automatically expose accelerators inside isolated containers.

D:

This underlying cause is associated with another GPU platform incident: Slurm reports some nodes as allocated indefinitely after a failed distributed job because job cleanup leaves Enroot container artifacts and stale GPU processes. The observed case instead concerns: An installed host driver does not automatically expose accelerators inside isolated containers.

Question 292

An infrastructure administrator receives the following incident: A Docker AI image launches on a GPU host but reports no CUDA devices because the NVIDIA container runtime integration was never installed. Which repeatable operational safeguard best prevents recurrence of this failure?

  1. Include container-to-GPU runtime integration tests in every NVIDIA host image acceptance plan.
  2. Include device-scoping and negative tenant-isolation tests in toolkit release validation.
  3. Run container library and device compatibility regressions after each host driver maintenance event.
  4. Record container CUDA requirements and enforce driver-capability checks during rollout.

Explanation

Correct Answer

A:

This preventive practice addresses the underlying failure (An installed host driver does not automatically expose accelerators inside isolated containers.) and supports continued assurance through: A small container runs the approved `nvidia-smi` or CUDA probe and reports the allocated device UUIDs correctly.

Incorrect Answers

B:

This prevention control targets another operational risk: A container in a multi-tenant GPU cluster can see accelerators allocated to another job because the runtime launches with unrestricted device exposure. The currently reported mechanism remains: An installed host driver does not automatically expose accelerators inside isolated containers.

C:

This prevention control targets another operational risk: After a driver upgrade, the container toolkit continues referencing stale GPU library paths, and a container loads incompatible runtime libraries. The currently reported mechanism remains: An installed host driver does not automatically expose accelerators inside isolated containers.

D:

This prevention control targets another operational risk: A container sees its host GPU but fails when run on a different node whose driver is older than the image's required CUDA feature level. The currently reported mechanism remains: An installed host driver does not automatically expose accelerators inside isolated containers.

Question 293

An NVIDIA accelerator platform readiness review identifies this issue: An operations team edits container runtime configuration by hand on some nodes, and CUDA containers work on one rack but fail after rescheduling to another rack. Which corrective deployment or operational action best addresses the stated problem?

  1. Apply version-controlled toolkit configuration and reconcile Docker or other runtime integrations across all target hosts.
  2. Reconcile toolkit and driver library integration using a supported configuration refresh and verify the intended runtime libraries.
  3. Configure the supported runtime and scheduler device allowlists so each container receives only its assigned GPU or MIG identifiers.
  4. Revoke the exposed key and replace embedded credentials with controlled secret injection and organization-approved registry access.

Explanation

Correct Answer

A:

For the stated NVIDIA AI infrastructure condition (An operations team edits container runtime configuration by hand on some nodes, and CUDA containers work on one rack but fail after rescheduling to another rack.), this action addresses the concrete failure: Apply version-controlled toolkit configuration and reconcile Docker or other runtime integrations across all target hosts. The underlying mechanism is: Inconsistent container-runtime GPU configuration causes workload failures independent of GPU hardware health. Verification would require: The same signed GPU container starts with matching device access on randomly sampled nodes across each rack.

Incorrect Answers

B:

This option could be appropriate for a different NVIDIA infrastructure problem (3.5: Install the NVIDIA Container Toolkit): After a driver upgrade, the container toolkit continues referencing stale GPU library paths, and a container loads incompatible runtime libraries. The present issue is more specifically caused by: Inconsistent container-runtime GPU configuration causes workload failures independent of GPU hardware health.

C:

This option could be appropriate for a different NVIDIA infrastructure problem (3.5: Install the NVIDIA Container Toolkit): A container in a multi-tenant GPU cluster can see accelerators allocated to another job because the runtime launches with unrestricted device exposure. The present issue is more specifically caused by: Inconsistent container-runtime GPU configuration causes workload failures independent of GPU hardware health.

D:

This option could be appropriate for a different NVIDIA infrastructure problem (3.7: Install NGC CLI on hosts): A training team scripts NGC pulls by embedding a personal API key directly in shell history and a Git repository, compromising a long-lived registry credential. The present issue is more specifically caused by: Inconsistent container-runtime GPU configuration causes workload failures independent of GPU hardware health.

Question 294

An NVIDIA accelerator platform readiness review identifies this issue: An operations team edits container runtime configuration by hand on some nodes, and CUDA containers work on one rack but fail after rescheduling to another rack. Which measured result most directly proves that this specific problem is resolved?

  1. The same signed GPU container starts with matching device access on randomly sampled nodes across each rack.
  2. A small container runs the approved `nvidia-smi` or CUDA probe and reports the allocated device UUIDs correctly.
  3. A negative tenant test cannot enumerate unauthorized accelerators while the permitted device runs the expected workload.
  4. A deliberately interrupted test job frees its GPU assignments and leaves the host ready for a new tenant after verified cleanup.

Explanation

Correct Answer

A:

This is the measurable acceptance result for the described case: The same signed GPU container starts with matching device access on randomly sampled nodes across each rack. It validates the corrective operation (Apply version-controlled toolkit configuration and reconcile Docker or other runtime integrations across all target hosts.) rather than a related but different system check.

Incorrect Answers

B:

This observation would help verify a different incident: A Docker AI image launches on a GPU host but reports no CUDA devices because the NVIDIA container runtime integration was never installed. It does not directly establish the required result for the current case: The same signed GPU container starts with matching device access on randomly sampled nodes across each rack.

C:

This observation would help verify a different incident: A container in a multi-tenant GPU cluster can see accelerators allocated to another job because the runtime launches with unrestricted device exposure. It does not directly establish the required result for the current case: The same signed GPU container starts with matching device access on randomly sampled nodes across each rack.

D:

This observation would help verify a different incident: Slurm reports some nodes as allocated indefinitely after a failed distributed job because job cleanup leaves Enroot container artifacts and stale GPU processes. It does not directly establish the required result for the current case: The same signed GPU container starts with matching device access on randomly sampled nodes across each rack.

Question 295

An NVIDIA accelerator platform readiness review identifies this issue: An operations team edits container runtime configuration by hand on some nodes, and CUDA containers work on one rack but fail after rescheduling to another rack. Which root cause or infrastructure principle best explains the observed failure?

  1. Containerized GPU applications depend on correct host-driver library injection as well as packaged userspace software.
  2. Inconsistent container-runtime GPU configuration causes workload failures independent of GPU hardware health.
  3. Default broad GPU visibility can violate resource allocation and isolation assumptions in shared container environments.
  4. Hardware discovery alone cannot ensure workload readiness when the control plane applies an incorrect provisioning profile.

Explanation

Correct Answer

B:

This technical mechanism explains the reported condition: Inconsistent container-runtime GPU configuration causes workload failures independent of GPU hardware health. The observed situation is: An operations team edits container runtime configuration by hand on some nodes, and CUDA containers work on one rack but fail after rescheduling to another rack. A requirement-aligned correction is: Apply version-controlled toolkit configuration and reconcile Docker or other runtime integrations across all target hosts.

Incorrect Answers

A:

This underlying cause is associated with another GPU platform incident: After a driver upgrade, the container toolkit continues referencing stale GPU library paths, and a container loads incompatible runtime libraries. The observed case instead concerns: Inconsistent container-runtime GPU configuration causes workload failures independent of GPU hardware health.

C:

This underlying cause is associated with another GPU platform incident: A container in a multi-tenant GPU cluster can see accelerators allocated to another job because the runtime launches with unrestricted device exposure. The observed case instead concerns: Inconsistent container-runtime GPU configuration causes workload failures independent of GPU hardware health.

D:

This underlying cause is associated with another GPU platform incident: A BCM provisioning environment discovers every compute node, yet new GPU hosts join the wrong category and receive CPU-only operating system images. The observed case instead concerns: Inconsistent container-runtime GPU configuration causes workload failures independent of GPU hardware health.

Question 296

An NVIDIA accelerator platform readiness review identifies this issue: An operations team edits container runtime configuration by hand on some nodes, and CUDA containers work on one rack but fail after rescheduling to another rack. Which repeatable operational safeguard best prevents recurrence of this failure?

  1. Audit toolkit runtime settings and perform multi-node container smoke tests during fleet rollouts.
  2. Include container-to-GPU runtime integration tests in every NVIDIA host image acceptance plan.
  3. Run container library and device compatibility regressions after each host driver maintenance event.
  4. Require access-policy review and negative scheduling tests before adding new GPU resource pools.

Explanation

Correct Answer

A:

This preventive practice addresses the underlying failure (Inconsistent container-runtime GPU configuration causes workload failures independent of GPU hardware health.) and supports continued assurance through: The same signed GPU container starts with matching device access on randomly sampled nodes across each rack.

Incorrect Answers

B:

This prevention control targets another operational risk: A Docker AI image launches on a GPU host but reports no CUDA devices because the NVIDIA container runtime integration was never installed. The currently reported mechanism remains: Inconsistent container-runtime GPU configuration causes workload failures independent of GPU hardware health.

C:

This prevention control targets another operational risk: After a driver upgrade, the container toolkit continues referencing stale GPU library paths, and a container loads incompatible runtime libraries. The currently reported mechanism remains: Inconsistent container-runtime GPU configuration causes workload failures independent of GPU hardware health.

D:

This prevention control targets another operational risk: A new node is added to the wrong Slurm partition and gains access to restricted customer workloads despite being intended for untrusted experimentation. The currently reported mechanism remains: Inconsistent container-runtime GPU configuration causes workload failures independent of GPU hardware health.

Question 297

During a GPU datacenter maintenance window, an engineer discovers: A container in a multi-tenant GPU cluster can see accelerators allocated to another job because the runtime launches with unrestricted device exposure. Which corrective deployment or operational action best addresses the stated problem?

  1. Apply version-controlled toolkit configuration and reconcile Docker or other runtime integrations across all target hosts.
  2. Install and configure the NVIDIA Container Toolkit for the supported container runtime and test GPU device injection.
  3. Configure the supported runtime and scheduler device allowlists so each container receives only its assigned GPU or MIG identifiers.
  4. Normalize node network interface configuration or select supported per-node fabric parameters through managed job profiles.

Explanation

Correct Answer

C:

For the stated NVIDIA AI infrastructure condition (A container in a multi-tenant GPU cluster can see accelerators allocated to another job because the runtime launches with unrestricted device exposure.), this action addresses the concrete failure: Configure the supported runtime and scheduler device allowlists so each container receives only its assigned GPU or MIG identifiers. The underlying mechanism is: Default broad GPU visibility can violate resource allocation and isolation assumptions in shared container environments. Verification would require: A negative tenant test cannot enumerate unauthorized accelerators while the permitted device runs the expected workload.

Incorrect Answers

A:

This option could be appropriate for a different NVIDIA infrastructure problem (3.5: Install the NVIDIA Container Toolkit): An operations team edits container runtime configuration by hand on some nodes, and CUDA containers work on one rack but fail after rescheduling to another rack. The present issue is more specifically caused by: Default broad GPU visibility can violate resource allocation and isolation assumptions in shared container environments.

B:

This option could be appropriate for a different NVIDIA infrastructure problem (3.5: Install the NVIDIA Container Toolkit): A Docker AI image launches on a GPU host but reports no CUDA devices because the NVIDIA container runtime integration was never installed. The present issue is more specifically caused by: Default broad GPU visibility can violate resource allocation and isolation assumptions in shared container environments.

D:

This option could be appropriate for a different NVIDIA infrastructure problem (3.3: Install clusters, categories, interfaces, Slurm, Enroot, and Pyxis): A multi-node NCCL job spans nodes with different fabric interface names, while Slurm launch scripts hard-code the interface present only on the first rack. The present issue is more specifically caused by: Default broad GPU visibility can violate resource allocation and isolation assumptions in shared container environments.

Question 298

During a GPU datacenter maintenance window, an engineer discovers: A container in a multi-tenant GPU cluster can see accelerators allocated to another job because the runtime launches with unrestricted device exposure. Which measured result most directly proves that this specific problem is resolved?

  1. The approved container resolves matching CUDA driver interfaces and completes a representative GPU task after the upgrade.
  2. The same signed GPU container starts with matching device access on randomly sampled nodes across each rack.
  3. A scheduled canary launches the approved container and accesses its allocated GPUs with successful startup logs and correct permissions.
  4. A negative tenant test cannot enumerate unauthorized accelerators while the permitted device runs the expected workload.

Explanation

Correct Answer

D:

This is the measurable acceptance result for the described case: A negative tenant test cannot enumerate unauthorized accelerators while the permitted device runs the expected workload. It validates the corrective operation (Configure the supported runtime and scheduler device allowlists so each container receives only its assigned GPU or MIG identifiers.) rather than a related but different system check.

Incorrect Answers

A:

This observation would help verify a different incident: After a driver upgrade, the container toolkit continues referencing stale GPU library paths, and a container loads incompatible runtime libraries. It does not directly establish the required result for the current case: A negative tenant test cannot enumerate unauthorized accelerators while the permitted device runs the expected workload.

B:

This observation would help verify a different incident: An operations team edits container runtime configuration by hand on some nodes, and CUDA containers work on one rack but fail after rescheduling to another rack. It does not directly establish the required result for the current case: A negative tenant test cannot enumerate unauthorized accelerators while the permitted device runs the expected workload.

C:

This observation would help verify a different incident: A training cluster stages a container with Enroot, but Slurm launches it without the intended Pyxis integration and the job cannot access accelerators. It does not directly establish the required result for the current case: A negative tenant test cannot enumerate unauthorized accelerators while the permitted device runs the expected workload.

Question 299

During a GPU datacenter maintenance window, an engineer discovers: A container in a multi-tenant GPU cluster can see accelerators allocated to another job because the runtime launches with unrestricted device exposure. Which root cause or infrastructure principle best explains the observed failure?

  1. An installed host driver does not automatically expose accelerators inside isolated containers.
  2. Inconsistent container-runtime GPU configuration causes workload failures independent of GPU hardware health.
  3. Default broad GPU visibility can violate resource allocation and isolation assumptions in shared container environments.
  4. Installing container runtimes locally does not ensure the scheduler is configured to invoke the correct container integration.

Explanation

Correct Answer

C:

This technical mechanism explains the reported condition: Default broad GPU visibility can violate resource allocation and isolation assumptions in shared container environments. The observed situation is: A container in a multi-tenant GPU cluster can see accelerators allocated to another job because the runtime launches with unrestricted device exposure. A requirement-aligned correction is: Configure the supported runtime and scheduler device allowlists so each container receives only its assigned GPU or MIG identifiers.

Incorrect Answers

A:

This underlying cause is associated with another GPU platform incident: A Docker AI image launches on a GPU host but reports no CUDA devices because the NVIDIA container runtime integration was never installed. The observed case instead concerns: Default broad GPU visibility can violate resource allocation and isolation assumptions in shared container environments.

B:

This underlying cause is associated with another GPU platform incident: An operations team edits container runtime configuration by hand on some nodes, and CUDA containers work on one rack but fail after rescheduling to another rack. The observed case instead concerns: Default broad GPU visibility can violate resource allocation and isolation assumptions in shared container environments.

D:

This underlying cause is associated with another GPU platform incident: A training cluster stages a container with Enroot, but Slurm launches it without the intended Pyxis integration and the job cannot access accelerators. The observed case instead concerns: Default broad GPU visibility can violate resource allocation and isolation assumptions in shared container environments.

Question 300

During a GPU datacenter maintenance window, an engineer discovers: A container in a multi-tenant GPU cluster can see accelerators allocated to another job because the runtime launches with unrestricted device exposure. Which repeatable operational safeguard best prevents recurrence of this failure?

  1. Run container library and device compatibility regressions after each host driver maintenance event.
  2. Include device-scoping and negative tenant-isolation tests in toolkit release validation.
  3. Include container-to-GPU runtime integration tests in every NVIDIA host image acceptance plan.
  4. Maintain tested host/DPU driver and DOCA version pairings with a documented recovery path.

Explanation

Correct Answer

B:

This preventive practice addresses the underlying failure (Default broad GPU visibility can violate resource allocation and isolation assumptions in shared container environments.) and supports continued assurance through: A negative tenant test cannot enumerate unauthorized accelerators while the permitted device runs the expected workload.

Incorrect Answers

A:

This prevention control targets another operational risk: After a driver upgrade, the container toolkit continues referencing stale GPU library paths, and a container loads incompatible runtime libraries. The currently reported mechanism remains: Default broad GPU visibility can violate resource allocation and isolation assumptions in shared container environments.

C:

This prevention control targets another operational risk: A Docker AI image launches on a GPU host but reports no CUDA devices because the NVIDIA container runtime integration was never installed. The currently reported mechanism remains: Default broad GPU visibility can violate resource allocation and isolation assumptions in shared container environments.

D:

This prevention control targets another operational risk: A BlueField DPU application fails after the host updates DOCA packages, while the DPU firmware and embedded software versions remain from an earlier release. The currently reported mechanism remains: Default broad GPU visibility can violate resource allocation and isolation assumptions in shared container environments.

NCP-AI Infrastructure (NCP-AII) Practice Tests