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NVIDIA NCP-AII Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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.
Topic 4
  • 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 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 (Q144-Q149):

NEW QUESTION # 144
Which software library provides GPU acceleration for pandas-like DataFrame operations?

Answer: B

Explanation:
RAPIDS cuDF provides GPU-accelerated DataFrame operations with a pandas-like API, enabling faster data processing on NVIDIA GPUs for analytics and AI workflows.


NEW QUESTION # 145
During HPL execution on a DGX cluster, the benchmark fails with "not enough memory' errors despite sufficient physical RAM. Which HPL.dat parameter adjustment is most effective?

Answer: C

Explanation:
The HPL problem size controls the benchmark's total memory footprint. Reducing the problem size lowers memory demand while keeping the block size stable, resolving "not enough memory" failures without unnecessarily changing the computational blocking behavior.


NEW QUESTION # 146
A single-node stress test fails during the PCle bandwidth validation phase. Which troubleshooting step is recommended first?

Answer: B

Explanation:
A PCIe bandwidth validation failure commonly points to a physical seating, link-training, or connection issue between the GPU and the server PCIe slot. Reseating the GPU and rerunning the test is the recommended first step because it directly addresses the most likely hardware/connection cause before changing BIOS settings or reinstalling software.


NEW QUESTION # 147
You are training a deep neural network using NCCL to coordinate communication across four GPUs in a single node. During early performance testing, you notice inconsistent scaling and longer-than-expected training times, even though all GPUs are being used. Which strategy would most effectively improve NCCL efficiency and collective operation performance in this setting?

Answer: C

Explanation:
The best strategy is to divide the batch evenly so each GPU receives a similar amount of work. NCCL collective operations, such as all-reduce, synchronize data across all participating GPUs. If one GPU receives more work than the others, the remaining GPUs may finish early and wait at synchronization points, creating poor scaling and longer training iterations. Balanced per-GPU batch sizes reduce straggler effects and allow computation and communication to proceed more predictably. Assigning the largest workload to one GPU creates imbalance and worsens synchronization delays. Disabling load balancing or allowing dynamic uneven assignment does not guarantee consistent work distribution. Increasing communication frequency with uneven workloads can increase overhead and make NCCL synchronization even less efficient. In a single-node DGX or HGX system, NCCL can take advantage of NVLink, NVSwitch, and PCIe topology, but application-level balance is still required. Proper batch partitioning, rank placement, GPU affinity, and framework configuration all help ensure that NCCL collectives complete efficiently and that all GPUs contribute evenly to each training step.


NEW QUESTION # 148
During a multi-day NeMo burn-in, intermittent "GPU fell off bus" errors occur. Which diagnostic approach isolates hardware faults?

Answer: B

Explanation:
DCGM diagnostics are designed to validate GPU health and detect hardware-related issues during stress or burn-in testing. Running DCGM alongside the NeMo burn-in helps correlate
"GPU fell off bus" events with GPU telemetry, PCIe issues, ECC errors, thermal behavior, or other hardware fault indicators.


NEW QUESTION # 149
......

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