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NVIDIA NCP-AII Exam Syllabus Topics:

Topic	Details
Topic 1	Troubleshoot and Optimize: Covers identifying and replacing faulty hardware components such as GPUs, network cards, and power supplies, along with performance optimization for AMD Intel servers and storage.
Topic 2	System and Server Bring-up: Covers end-to-end physical setup of GPU-based AI infrastructure, including BMC OOB TPM configuration, firmware upgrades, hardware installation, and power and cooling validation to ensure servers are workload-ready.
Topic 3	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 4	Cluster Test and Verification: Covers full cluster validation through HPL and NCCL benchmarks, NVLink and fabric bandwidth tests, cable and firmware checks, and burn-in testing using HPL, NCCL, and NeMo.
Topic 5	Control Plane Installation and Configuration: Covers deploying the software stack including Base Command Manager, OS, Slurm Enroot Pyxis, NVIDIA GPU and DOCA drivers, container toolkit, and NGC CLI.

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NVIDIA AI Infrastructure Sample Questions (Q170-Q175):

NEW QUESTION # 170
When verifying network cable signal integrity during cluster deployment, which measurement result most strongly indicates a cable signal problem?

A. Output of ifconfig showing link speed at the expected rate on both ends of the cable
B. Network pings between all cluster nodes return responses with delays under 2 ms on a 100Gb network.
C. Repeated CRC errors and intermittent port flapping reported by switch counters.

Answer: C

Explanation:
Repeated CRC errors and intermittent port flapping are strong indicators of poor cable signal integrity. These symptoms show that the link is experiencing physical-layer errors or instability, which can cause packet loss, retransmissions, and degraded cluster performance under load.

NEW QUESTION # 171
A user wants to restrict a Docker container to use only GPUs 0 and 2. Which command achieves this?

A. docker run -gpus "device=0.2" nvidia/cuda:12.1-base nvidia-smi
B. docker run -e NVIDIA_VISIBLE_DEVICES=0.2 nvidia/cuda:12.1-base nvidia-smi
C. docker run -gpus all nvidia/cuda:12.1-base nvidia-smi -id=0,2
D. docker run -device /dev/nvidia0./dev/nvidia2 nvidia/cuda:12.1-base nvidia-smi

Answer: A

Explanation:
Docker GPU access can be restricted with the --gpus flag by specifying the target GPU device IDs. Using the device filter limits the container so it can see and use only GPUs 0 and 2 rather than all GPUs on the host.

NEW QUESTION # 172
An enterprise builds a Kubernetes-based AI platform using NVIDIA GPU Operator. After adding several new GPU nodes, administrators notice that NVIDIA drivers, container runtime components, and monitoring agents are installed automatically without manual intervention.
Which capability of GPU Operator enables this behavior?

A. Dynamic migration of virtual machines between GPU nodes
B. Automatic firmware flashing of GPUs
C. Automatic BIOS configuration for every worker node
D. Lifecycle management of GPU software components through Kubernetes

Answer: D

Explanation:
GPU Operator simplifies Kubernetes deployments by managing GPU drivers, the NVIDIA Container Toolkit, device plugins, DCGM exporters, and related software as Kubernetes resources. This automates installation, upgrades, and lifecycle management across the cluster. It does not configure BIOS settings or perform virtual machine migration.

NEW QUESTION # 173
A data scientist reports that training performance on a DGX A100 server has significantly degraded over the past week. 'nvidia-smi' shows all GPUs functioning, but 'nvprof' reveals substantially increased 'cudaMemcpy' times. What is the MOST likely bottleneck?

A. The NVLink connections between GPUs are failing, forcing data transfers through PCle.
B. The CPU is heavily loaded, causing contention for system memory bandwidth.
C. The GPUs are overheating, causing thermal throttling and slower memory transfers.
D. The storage system is slow, delaying data loading and preprocessing.
E. The PCle bus is saturated, limiting data transfer speeds between the CPU and GPUs.

Answer: B

Explanation:
Increased ScudaMemcpy' times indicate a bottleneck in data transfer between the CPU and GPIJs or within the GPU memory itself. While PCle saturation or failing NVLink connections could contribute, a heavily loaded CPU is often the primary culprit. CPU-bound preprocessing tasks or general system load can create contention for system memory, slowing down data movement to the GPUs. The NVLink being used is less likely because 'nvidia-smi' would likely report errors or lower speeds between the GPUs

NEW QUESTION # 174
Consider a scenario where you need to isolate GPU workloads in a multi-tenant Kubernetes cluster. Which of the following Kubernetes constructs would be MOST suitable for achieving strong isolation at both the resource and network level?

A. Using taints and tolerations to dedicate GPU nodes to specific workloads.
B. Using pod affinity and anti-affinity rules to control pod placement.
C. Using namespaces with resource quotas and network policies.
D. Using labels and selectors to schedule workloads on specific GPU nodes.
E. Using node affinity only.

Answer: C

Explanation:
Namespaces provide logical isolation within a Kubernetes cluster. Resource quotas limit the resources (including GPIJs) that a namespace can consume, while network policies control network traffic between namespaces, ensuring strong isolation. Options B, C, D, and E provide some level of control over pod placement but do not offer the same level of resource and network isolation as namespaces with resource quotas and network policies.

NEW QUESTION # 175
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