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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
  • 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
  • Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
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 Valid Test Topics & NCP-AII Free Download Demo & NVIDIA AI Infrastructure Practice Test Training

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NVIDIA AI Infrastructure Sample Questions (Q185-Q190):

NEW QUESTION # 185
A system administrator notices a DGX A100 has lost a power supply, but the system is still operating at full performance. What is the minimum number of power supplies needed for the system to operate at full redundancy?

Answer: B

Explanation:
DGX A100 systems require a minimum of four functioning power supplies to maintain full redundancy and full-performance operation. This allows the system to continue operating reliably even if a power supply failure occurs.


NEW QUESTION # 186
You are validating the environment of an NVIDIA GPU-accelerated data center during post-deployment checks. Which one action is essential to confirm that power and cooling are sufficient for the stable operation of NVIDIA DGX H100 systems?

Answer: D

Explanation:
Stable operation of high-density AI infrastructure like the DGX H100 requires strict adherence to power and thermal specifications. A single DGX H100 system can draw up to10.2kWunder peak load. Therefore, the most essential validation step is ensuring the electrical "infrastructure-to-server" handoff is healthy. This involves verifying that the system is connected to redundant PDUs (Power Distribution Units) capable of handling the amperage requirements without tripping breakers. UsingNVSM (NVIDIA System Management), an administrator must check that all six power supplies (PSUs) are functional and receiving nominal input voltage (typically 200V-240V). If a PSU reports sub-optimal input or a "Loss of Redundancy," the system may throttle performance or shut down unexpectedly during a heavy training run. Fans running at
100% (Option A) at all times would actually indicate an inefficient or failed cooling policy, as fans should dynamically scale based on thermals. Overclocking (Option B) is not supported or recommended for enterprise DGX systems, as they are already factory-tuned for the highest stable performance.


NEW QUESTION # 187
A BlueField-3 DPUis configured to run both control plane and data plane functions. After a recent software update, you notice that the data plane performance has significantly degraded, but the control plane remains responsive. What is the MOST likely cause, assuming the update didn't introduce any code bugs, and what is the BEST approach to diagnose this issue?

Answer: E

Explanation:
Resource contention is the MOST likely cause, assuming no code bugs. The update may have increased the resource demands of either the control or data plane, leading to contention. Profiling the data plane processes with 'perf or 'bpftrace' helps pinpoint the bottlenecks. Downgrading drivers or reflashing firmware are more drastic steps to take after confirming resource contention isn't the issue.


NEW QUESTION # 188
A financial services firm is deploying an AI model for fraud detection that requires rapid inference and data retrieval across multiple sites. Which feature should their storage system prioritize?

Answer: A

Explanation:
Fraud detection inference across multiple sites needs fast access to data and flexibility for different workloads and applications. Multi-protocol storage with low latency supports rapid retrieval, real-time inference pipelines, and distributed access patterns required for time-sensitive AI fraud detection.


NEW QUESTION # 189
An InfiniBand server stops working, and a system administrator runs the "ibstat" command that provides the following output:

What is the cause of the issue?

Answer: B

Explanation:
The port shows Physical state: LinkUp, so the cable and physical link are up, but the logical State remains Initializing and SM lid is 0. In InfiniBand, a port typically remains initializing when no subnet manager is running or reachable to bring the port to the active state.


NEW QUESTION # 190
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