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NVIDIA NCP-AII Exam Syllabus Topics:

Topic	Details
Topic 1	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.
Topic 2	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 3	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 4	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 5	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.

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NVIDIA AI Infrastructure Sample Questions (Q155-Q160):

NEW QUESTION # 155
Which function is used to collect the cluster counters information?

A. SM
B. 0.5
C. GM
D. 0.041666667

Answer: C

Explanation:
GM is the function used to collect cluster counters information, aggregating fabric-level counter data for cluster monitoring and analysis.

NEW QUESTION # 156
During a DGX cluster deployment, what is the most effective way to verify the health and integrity of the local RAID storage array?

A. Run a read/write benchmark utility, such as FIO, across the RAID array, looking for expected speed and latency metrics as proof of storage integrity.
B. Verify that all configured RAID volumes are mounted and available in the operating system, and that disk utilization levels are within recommended limits.
C. Use the mdadm --examine and mdadm --detail commands to review the RAID array's status, checking for drive failures, array consistency, and error events.

Answer: C

Explanation:
The correct answer is C because mdadm --examine and mdadm --detail directly inspect Linux software RAID metadata and array health. During DGX cluster deployment, local RAID storage must be verified before the node is released for AI workloads, logging, container images, datasets, scratch space, or checkpoint staging. A mounted filesystem only proves that the operating system can access the volume; it does not confirm that the RAID array is clean, synchronized, degraded, rebuilding, or free of disk failure events. FIO is useful for performance benchmarking, but speed and latency results alone do not prove RAID integrity. A degraded array can sometimes still deliver acceptable short-term performance while lacking redundancy or carrying latent errors. The mdadm --detail command reports array state, active devices, failed devices, rebuild progress, spare disks, and consistency status. The mdadm --examine command checks member-device metadata and helps confirm whether disks belong correctly to the array. In NVIDIA AI infrastructure bring- up, RAID health validation is part of server readiness because storage faults can cause job failures, lost logs, unstable container operation, or checkpoint corruption during production workloads.

NEW QUESTION # 157
An enterprise is designing a new AI training environment expected to expand from 32 GPUs to more than 1,000 GPUs within two years. Management wants to minimize future architectural changes while maintaining high throughput for distributed training. Which infrastructure design strategy best supports these long-term objectives?

A. Deploy standard office Ethernet switches because bandwidth can be increased later through software updates.
B. Maximize the number of GPUs in a single server to eliminate networking requirements.
C. Build the environment around a scalable high-speed fabric with modular expansion capabilities.
D. Connect every server directly to every other server using dedicated cables.

Answer: C

Explanation:
Large AI clusters should be designed around scalable, high-bandwidth fabrics such as InfiniBand or optimized Ethernet with RoCE. Modular network architectures allow additional compute nodes to be integrated without redesigning the infrastructure. Direct server-to-server cabling does not scale, while relying solely on larger servers limits future growth and flexibility.

NEW QUESTION # 158
A system administrator needs to install a GPU/DPU in a server. The server has a free PCI-e slot, there are enough free PCI-e lanes, and there is enough room for the card. Which procedure should be followed?

A. Ensure the server has enough power. Verify compatibility of cables with server's platform. Make sure the server is down to remove cables safely. Wear an ESD bracelet.
B. Ensure the server has enough power. Make sure the server is up and running with attached cables. Wear an ESD bracelet.
C. Ensure the server has enough power. Verify compatibility of cables with server's platform. Make sure the server is down to remove cables safely. Do not wear an ESD bracelet.
D. Ensure the server has enough power. Make sure the server is down to remove cables safely.Wear an ESD bracelet.

Answer: A

Explanation:
Installing a GPU or DPU requires confirming sufficient power, verifying that the required cables are compatible with the server platform, powering the server down before removing or connecting hardware, and wearing an ESD bracelet to prevent electrostatic damage to the card.

NEW QUESTION # 159
A cluster administrator deploys GPUDirect RDMA for a distributed AI training environment. After deployment, GPU utilization improves while CPU utilization during communication decreases substantially. Which architectural change most directly explains this improvement?

A. All communication is redirected through NVSwitch.
B. PCIe transactions are replaced entirely by Ethernet broadcasts.
C. Data transfers bypass CPU memory and move directly between GPUs and network adapters.
D. Network traffic is compressed before entering GPU memory.

Answer: C

Explanation:
GPUDirect RDMA allows compatible network adapters to access GPU memory directly, eliminating unnecessary copies through host memory and reducing CPU involvement. This lowers latency, decreases CPU overhead, and improves overall communication efficiency during distributed AI workloads. NVSwitch only affects intra-server communication, while GPUDirect RDMA operates across network fabrics.

NEW QUESTION # 160
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