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NVIDIA NCP-AII Exam Syllabus Topics:

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

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NVIDIA AI Infrastructure Sample Questions (Q91-Q96):

NEW QUESTION # 91
A system administrator is responsible for managing an NVIDIA SuperPOD. The administrator wants to verify that all systems are in a healthy state. What should the system administrator do?

Answer: A

Explanation:
For NVIDIA SuperPOD operations, administrators should use vendor-provided health monitoring and management tools to verify hardware status across all systems and configure alerting for failures or degraded components. This provides scalable, proactive visibility into system health instead of relying on manual checks.


NEW QUESTION # 92
You are responsible for ensuring interoperability between AI applications deployed across a diverse IT landscape, including an on-premises data center equipped with NVIDIA GPUs and multiple cloud platforms from different vendors. These environments need to support complex AI workflows that involve large-scale data processing, real-time analytics, and machine learning model training. To maintain consistent performance and flexibility, which strategy should you prioritize?

Answer: A

Explanation:
Compatible storage protocols and APIs such as NFS and S3 allow AI applications, data pipelines, and analytics workflows to access and exchange data consistently across on-premises GPU infrastructure and multiple cloud platforms. This supports interoperability, portability, and flexible integration across diverse environments.


NEW QUESTION # 93
An InfiniBand administrator needs to run performance benchmarks on new devices added to the fabric. What tool should be used to check the latency?

Answer: A

Explanation:
While the InfiniBand fabric is known for high bandwidth, its defining characteristic for AI workloads is ultra- low, sub-microsecond latency. When new nodes or switches are added, administrators must verify that the point-to-point latency meets the hardware specifications. The ib_write_lat utility is the standard micro- benchmark from the perftest suite used for this purpose. It measures the time it takes to complete an RDMA Write operation between two nodes. This tool is "verified" because it operates directly over the InfiniBand Verbs layer, bypassing the CPU overhead of the standard TCP/IP stack. Unlike tcpdump (Option A), which is used for packet capture, or ibdiagnet (Option C), which is used for fabric-wide discovery and error reporting, ib_write_lat provides a granular, nanosecond-level measurement of the link's responsiveness. In an AI cluster, even a small increase in latency can cause a "straggler" effect in distributed training, where all GPUs wait for the slowest link to complete a synchronization step.


NEW QUESTION # 94
You are an infrastructure engineer tasked with validating a new AI training cluster before releasing it to users. Your team wants to perform a NeMo burn-in to ensure both hardware and software are reliable and ready for production workloads. Which of the following actions are required as part of a proper NeMo burn-in process? (Choose two.)

Answer: B,D

Explanation:
A proper NeMo burn-in uses a representative NeMo training or pretraining workload configured to run across the target GPUs and nodes. Running that workload for an extended duration while monitoring errors, stalls, GPU behavior, and performance stability validates that the hardware, drivers, containers, networking, and software stack are production-ready.


NEW QUESTION # 95
You are planning the network infrastructure for a DGX SuperPOD. You need to ensure that the network fabric can handle the high bandwidth and low latency requirements of A1 training workloads. Which network technology is the RECOMMENDED choice for interconnecting the DGX nodes within the SuperPOD, and why?

Answer: C

Explanation:
InfiniBand is the recommended network technology for DGX SuperPODs due to its high bandwidth, low latency, and support for RDMA (Remote Direct Memory Access). RDMA allows GPIJs to directly access each other's memory without involving the CPU, significantly reducing latency and improving performance for distributed A1 training workloads. Ethernet, even at higher speeds, generally doesn't offer the same level of performance and RDMA capabilities as InfiniBand.


NEW QUESTION # 96
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