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NVIDIA NCP-AII Prüfungsplan:

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

>> NCP-AII Musterprüfungsfragen <<

NCP-AII Zertifikatsdemo, NCP-AII Prüfungsmaterialien

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NVIDIA AI Infrastructure NCP-AII Prüfungsfragen mit Lösungen (Q176-Q181):

176. Frage
An engineer needs to completely remove NVIDIA GPU drivers from an Ubuntu 22.04 system to troubleshoot conflicts. Which command sequence ensures all driver components are purged?

Antwort: B

Begründung:
The correct command sequence is sudo apt-get purge nvidia-* & & sudo apt-get autoremove. Purging removes NVIDIA driver packages and their configuration files, while autoremove removes dependency packages that were installed automatically and are no longer required. NVIDIA's current data center driver removal guidance for Ubuntu 22.04 and 24.04 uses apt remove --autoremove --purge with NVIDIA-related package patterns, including driver, Fabric Manager, firmware, headless, DKMS, compute, decode, encode, and related libraries. A simple apt-get remove nvidia-driver-550 removes only one package and may leave DKMS modules, libraries, configuration files, Fabric Manager packages, or version-specific dependencies behind. Manually deleting /usr/lib/nvidia is unsafe because it bypasses package management and can leave the system in an inconsistent state. ubuntu-drivers uninstall is not the most complete answer for fully purging all NVIDIA driver components during conflict troubleshooting. After removal, administrators should verify loaded modules, package state, and reboot before installing the intended validated driver stack. Clean removal is important in AI infrastructure because driver conflicts can break CUDA, NCCL, DCGM, Fabric Manager, and container runtime behavior.


177. Frage
During East-West fabric validation on a 64-GPU cluster, an engineer runs all_reduce_perf and observes an algorithm bandwidth of 350 GB/s and bus bandwidth of 656 GB/s. What does this indicate about the fabric performance?

Antwort: B

Begründung:
When evaluating NVIDIA Collective Communications Library (NCCL) performance, it is vital to distinguish betweenAlgorithm BandwidthandBus Bandwidth. For an all_reduce operation, the Bus Bandwidth represents the effective data transfer rate across the hardware links, which includes the overhead of the ring or tree collective algorithm. In an NDR (400G) InfiniBand fabric, the theoretical peak per link is 50 GB/s (unidirectional). In a 64-GPU cluster (8 nodes of 8 GPUs), achieving a bus bandwidth of 656 GB/s indicates that the fabric is efficiently utilizing the multiple 400G rails available on the DGX H100. This result is considered optimal as it reflects near-line-rate performance when accounting for network headers and synchronization overhead. Algorithm bandwidth is naturally lower because it represents the "useful" data moved from the application's perspective. If the bus bandwidth were significantly lower, it would suggest congestion, cable faults, or sub-optimal routing.


178. Frage
Which protocol is commonly used in Spine-Leaf architectures for dynamic routing and load balancing across multiple paths?

Antwort: A

Begründung:
ECMP (Equal-Cost Multi-Path) is crucial for efficiently utilizing the multiple paths available in a Spine-Leaf architecture. It allows traffic to be distributed across these paths, improving throughput and reducing congestion. OSPF and BGP can be used for routing but do not inherently provide per-packet load balancing. STP is used to prevent loops, and VRRP provides router redundancy, neither of which directly address load balancing across multiple equal-cost paths.


179. Frage
ClusterKit's NCCL bandwidth test shows 350 GB/s on a 400G InfiniBand fabric. How should this result be interpreted?

Antwort: D

Begründung:
The result should be interpreted as optimal in the context of the provided ClusterKit NCCL bandwidth test.
ClusterKit is designed for high-performance cluster validation and includes GPU communication tests such as GPU-GPU latency, GPU-GPU bandwidth, GPU-host latency, and NCCL bandwidth and latency. A reported
350 GB/s value in this type of question normally represents aggregate NCCL communication bandwidth across GPUs, not the raw line rate of a single 400G network port. If ClusterKit reports this result as part of a healthy NCCL bandwidth run, it indicates that the GPU communication path, RDMA stack, and fabric are performing as expected. Option A is incorrect because it incorrectly references HDR and assumes a single- link expectation above 390 GB/s. Option B incorrectly jumps to FEC tuning without evidence of link errors, retransmissions, or degraded counters. Option D is also incorrect because CPU stress testing does not validate NCCL GPU-to-GPU fabric bandwidth. In production validation, this result should still be considered alongside consistency, error counters, NCCL logs, topology, and reference values for the exact DGX, GPU, HCA, and switch configuration.


180. Frage
A user reports that their CUDA application is running slower than expected after an NVIDIA driver update. You suspect a driver compatibility issue. How can you revert to a previous NVIDIA driver version on an Ubuntu system, assuming you have the older driver package 'nvidia-driver-470 470.82.00-0ubuntu1_amd64.deb'?

Antwort: A

Begründung:
The safest and most reliable approach is to first remove the current NVIDIA driver using 'sudo apt purge nvidia- to avoid conflicts. Then, install the .deb' package using 'sudo dpkg -i' and resolve any potential dependency issues with 'sudo apt -fix-broken install'. 'nvidia-smi' cannot downgrade drivers. Editing "/etc/apt/sources.list' can be risky and lead to system instability. Directly installing with 'dpkg -i' without purging the old driver can cause conflicts. If you installed with .run' before, then you must uninstall using that method first.


181. Frage
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