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NVIDIA NCP-AII Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 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 (Q119-Q124):

NEW QUESTION # 119
Which command is used to verify the installation and configuration of the NGC CLI after initial setup?

A. 'ngc -version'
B. 'ngc config validate'
C. 'ngc registry model list'
D. 'ngc setup verify'
E. 'ngc system statue'

Answer: A,C

Explanation:
'ngc -version' confirms the CLI is installed and shows its version. 'ngc registry model list' verifies that the CLI can authenticate with NGC and access the registry.

NEW QUESTION # 120
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?

A. Network misconfiguration; Verify the MTU and QOS settings on the network interfaces.
B. Driver incompatibility; Downgrade the Mellanox OFED drivers to the previous version.
C. Firmware corruption; Re-flash the BlueField DPIJ with the latest firmware image.
D. Resource contention; Use 'perf or 'bpftrace' to profile the data plane processes and identify resource bottlenecks (CPU, memory, cache).
E. Power throttling; Check the DPU's power consumption and thermal status via the BMC.

Answer: D

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 # 121
Consider this Python code snippet used for loading data for a deep learning model:

Assuming the images are stored on a remote file system accessed over NFS, and training is slow due to I/O bottlenecks, which change to the 'DataLoader' would MOST directly improve performance?

A. Increase 'batch_size' significantly (e.g., to 256).
B. Increase 'num_workers' to a value equal to the number of CPU cores on each training node.
C. Set in the DataLoader.
D. Implement multithreaded data loading inside _getitem
E. Decrease 'num workers' to 1.

Answer: B

Explanation:
Explanation: Increasing 'num_workerS allows the DataLoader to load data in parallel, which can help to overcome the I/O bottleneck when using NFS. Setting it to the number of CPU cores on each training node often yields the best performance. Increasing 'batch_size' helps, but it has diminishing returns if I/O is the primary bottleneck. Decreasing 'num_workers' will worsen the bottleneck. Primarily improves GPU memory transfer performance, not I/O performance from NFS. Multithreading inside _getitem is more complex and can lead to contention if not implemented carefully.

NEW QUESTION # 122
During a 72-hour HPL burn-in test on a DGX H100 cluster, one node shows a 15% performance drop after 48 hours. What are the two most likely causes and diagnostic steps?
Pick the 2 correct responses below.

A. MPI configuration error; rerun with --cpu-affinity adjustments.
B. Network packet loss; analyze ibdiagnet reports.
C. Thermal throttling due to cooling issues; check nvidia-smi dmon.
D. Memory corruption; reboot the node and reduce problem size N.

Answer: B,C

Explanation:
The two most likely causes are network packet loss and thermal throttling. A performance drop after 48 hours of HPL burn-in is less likely to be a simple launch-time MPI configuration issue, because MPI affinity errors usually appear from the beginning of the run as consistently poor performance. A delayed degradation suggests the system changed state under sustained load. Thermal throttling is a common cause: after many hours, rack cooling imbalance, blocked airflow, high inlet temperature, or fan behavior can cause GPU clocks to drop. nvidia-smi dmon helps monitor GPU temperature, power, utilization, and clocks over time. Network packet loss is also likely in multi-node HPL because HPL depends on heavy communication across the InfiniBand fabric. Link errors, symbol errors, retransmissions, degraded cables, or congestion can reduce sustained performance. ibdiagnet is the correct fabric-level diagnostic tool to collect and analyze InfiniBand health, topology, counters, and link issues. Rebooting and reducing matrix size would hide the symptom rather than diagnose it. Correct burn-in practice is to preserve evidence, inspect thermal telemetry, review network diagnostics, and compare the affected node against healthy peers.

NEW QUESTION # 123
You have an Intel Xeon Gold server with 2 NVIDIA Tesla VI 00 GPUs. After deploying your A1 application, you observe that one GPU is consistently running at a significantly higher temperature than the other What could be a plausible reason for this behavior?

A. The server's airflow is inadequate, causing poor cooling for one of the GPUs.
B. One GPU is defective and drawing excessive power.
C. The ambient temperature in the server room is higher on one side of the rack.
D. One GPU's driver version is outdated, leading to inefficient power management.
E. The workload is not evenly distributed between the GPUs, causing one GPU to be more heavily utilized.

Answer: A,E

Explanation:
Uneven heat distribution often points to airflow problems or unbalanced workloads. Inadequate airflow can cause localized hotspots. Uneven workload distribution will naturally cause one GPU to work harder and generate more heat. While a defective GPU or driver issues are possibilities, they are less likely than airflow and workload imbalances in this scenario. High ambient temperature is also a contributing factor but less direct.

NEW QUESTION # 124
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