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NVIDIA NCP-AII Exam Syllabus Topics:

TopicDetails
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
  • 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
  • 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 4
  • 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 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 (Q24-Q29):

NEW QUESTION # 24
During multi-node HPL burn-in, GPUs show uneven utilization. Which configuration ensures balanced workload distribution?

Answer: A

Explanation:
Uneven GPU utilization in a multi-node cluster is a classic symptom ofPCIe/NUMA imbalance. In a DGX H100, each set of GPUs is physically closer to a specific CPU socket and a specific set of high-speed NICs. If the workload (HPL) is launched without strict affinity, an MPI rank running on a CPU core attached to Socket
0 might attempt to control a GPU attached to Socket 1. This forces data to cross the inter-processor links (UPI
/QPI), which have significantly higher latency and lower bandwidth than a direct PCIe path. This
"bottlenecking" causes some GPUs to wait for data longer than others, leading to the uneven utilization observed. The verified solution is to use an orchestration script or flags that enforceAffinity. By setting --gpu- affinity and --cpu-affinity, the administrator ensures that each GPU is managed by a CPU core on its local NUMA node. This alignment minimizes latency and ensures that every GPU in the cluster receives data at the same rate, resulting in the flat, high-utilization profile required for a successful HPL burn-in record.


NEW QUESTION # 25
An administrator needs to manually deploy the BlueField image on a target DPU. The administrator downloads the new image file and needs to flash it to the hardware. Which command should the administrator use?

Answer: C

Explanation:
The correct command is bfb-install --rshim, normally used with the BFB image path and the appropriate RShim device, such as sudo bfb-install --rshim rshim0 --bfb < image_path.bfb > . BlueField software images are commonly deployed as BFB files, and RShim provides the host-side path used to push the image to the BlueField device. NVIDIA documentation states that the bfb-install utility is included with the RShim package and is used to push the BFB image to the BlueField side while reporting installation progress. The mlnx_fw_updater.pl tool is for firmware updates, not full BlueField OS image deployment. apt install doca- runtime installs DOCA runtime packages but does not flash a BlueField image. Using dd directly to a made- up device path is unsafe and not the supported method for deploying a BlueField boot stream image. During bring-up, using the supported BFB installation workflow helps ensure the DPU boots a valid signed image and enters a known operational state.


NEW QUESTION # 26
A media company is developing an AI platform for video content analysis that requires storing and processing large volumes of unstructured video data. The platform must support high throughput for data ingestion and provide efficient access for real-time analytics. Given these requirements, which storage strategy should the company implement?

Answer: A

Explanation:
Object storage is best suited for large volumes of unstructured video data because it scales efficiently, supports high-throughput ingestion, and uses rich metadata to organize and retrieve content for analytics workflows. This makes it a strong fit for AI-based video analysis and real- time data access patterns.


NEW QUESTION # 27
You need to remotely monitor the GPU temperature and utilization of a server without installing any additional software on the server itself. Assuming you have network access to the server's BMC (Baseboard Management Controller), which protocol and standard data format would BEST facilitate this?

Answer: D

Explanation:
IPMI is a standard interface for out-of-band server management, commonly used for monitoring hardware sensors like temperature and utilization. BMCs typically support IPMI. SDRs are the data format used by IPMI for sensor data. SNMP is also an option, but IPMI is more directly tied to hardware monitoring. The rest are less efficient or require additional software installation.


NEW QUESTION # 28
Your company is planning to expand its AI capabilities significantly over the next five years. To future-proof your storage infrastructure, you need a solution that can scale in both capacity and performance. Which of the following strategies best ensures that your storage infrastructure remains adaptable to future AI demands?

Answer: A

Explanation:
A hybrid cloud model provides the flexibility to scale capacity and performance over time by combining on-premises infrastructure for predictable, high-performance workloads with cloud resources for elastic growth. This approach keeps the storage architecture adaptable as AI data volumes, training workloads, and performance requirements increase.


NEW QUESTION # 29
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