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NVIDIA NCP-AII Exam Syllabus Topics:

Section	Weight	Objectives
Troubleshoot and Optimize	12%	- Fault detection and remediation 1. GPU, fan, network card fault identification 2. Replacement of faulty hardware components - Performance optimization 1. Server performance tuning (Intel/AMD platforms) 2. Storage optimization
Physical Layer Management	5%	- Networking and GPU partitioning 1. BlueField network platform configuration 2. MIG (Multi-Instance GPU) configuration
Control Plane Installation and Configuration	19%	- GPU utilization in containers 1. Docker GPU usage validation - Drivers and toolkits 1. NGC CLI deployment 2. NVIDIA container toolkit installation 3. NVIDIA GPU and DOCA drivers installation/update - Infrastructure software stack deployment 1. Operating system installation 2. Cluster setup (Slurm, Enroot, Pyxis) 3. Base Command Manager (BCM) installation and HA configuration
Cluster Test and Verification	33%	- Performance and stress testing 1. HPL (High-Performance Linpack) benchmarking 2. Single-node stress testing 3. Cluster burn-in tests (HPL, NCCL, NeMo) 4. NCCL communication testing - Network and hardware validation 1. NVLink validation 2. Firmware validation (switches, transceivers, BlueField) 3. Cabling and signal verification - Cluster diagnostics 1. Storage testing 2. ClusterKit multi-node assessment
System and Server Bring-up	31%	- Deployment and validation lifecycle 1. Sequence of deployment and validation events 2. Network topologies for AI factories - Physical infrastructure validation 1. Cable and transceiver types validation 2. Storage parameter initialization 3. Power and cooling validation - Hardware initialization and configuration 1. Firmware upgrades including HGX and fault detection 2. GPU server installation and validation 3. Hardware validation for workloads 4. BMC, OOB, and TPM initial configuration

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NVIDIA AI Infrastructure Sample Questions (Q137-Q142):

NEW QUESTION # 137
You are installing four NVIDIAAIOO GPUs into a server designed for AI training. The server motherboard has multiple PCIe Gen4 x16 slots. However, the server's power supply unit (PSU) only has three 8-pin PCIe power connectors available. What is the BEST course of action to ensure all GPUs receive adequate power?

A. Underclock the GPUs significantly to reduce their power consumption below the available PSU capacity.
B. Connect the GPUs using the motherboard's internal SATA power connectors.
C. Install only three GPUs and leave the fourth unpowered.
D. Replace the existing PSU with a higher wattage PSU that has at least four 8-pin PCIe power connectors.
E. Use a PCIe power splitter cable on one of the 8-pin connectors to power two GPUs.

Answer: D

Explanation:
Using a splitter can overload the original connector and cause instability or damage. Leaving a GPU unpowered defeats the purpose. SATA connectors are not designed for the high power requirements of GPUs. Underclocking might work, but is not a reliable long-term solution. Replacing the PSU is the safest and most reliable solution.

NEW QUESTION # 138
You are managing a cluster of GPU servers for deep learning. You observe that one server consistently exhibits high GPU temperature during training, causing thermal throttling and reduced performance. You've already ensured adequate airflow. Which of the following actions would be MOST effective in addressing this issue?

A. Re-seat the GPU in its PCle slot to ensure proper contact and heat dissipation.
B. Update the NVIDIA drivers to the latest version.
C. Increase the fan speed of the GPU cooler using 'nvidia-smi --fan'.
D. Reduce the ambient temperature of the data center.
E. Lower the GPU power limit using 'nvidia-smi -power-limit*.

Answer: A,C

Explanation:
Re-seating the GPU (D) ensures a proper connection between the GPU and the motherboard, which is crucial for effective heat dissipation. Increasing fan speed (E) can directly improve cooling. Lowering the power limit (B) reduces temperature but also reduces performance. Updating drivers (C) may help in some cases, but it is less likely to solve a thermal throttling problem. Lowering the ambient temperature (A) is generally beneficial but might not be specific enough to fix the overheating issue on a single server.

NEW QUESTION # 139
Which of the following is the MOST critical consideration when planning the cooling strategy for a server rack containing multiple NVIDIA A100 GPUs?

A. Ensuring the server room temperature is kept below 25 degrees Celsius.
B. Using liquid cooling for the CPUs, but air cooling for the GPUs.
C. Applying thermal paste to the GPU memory chips.
D. Optimizing airflow to ensure hot air is efficiently exhausted from the rack and cool air is drawn in.
E. Increasing the fan speed of the server chassis fans to maximum.

Answer: D

Explanation:
While ambient temperature is important, optimized airflow is crucial for removing the heat generated by the GPUs. Focusing solely on CPU cooling neglects the GPU heat. Applying thermal paste to memory chips is generally unnecessary unless specifically recommended by the manufacturer. Maximizing fan speed can help, but efficient airflow design is more effective.

NEW QUESTION # 140
An engineer needs to validate 400G DAC cable signal integrity in a DGX cluster. Which CVT metric best identifies marginal cables needing replacement?

A. Transceiver model matching QSFP-DD specifications.
B. Temperature fluctuations > 5°C during validation.
C. Effective BER > 1.5E-254 during a ≤6-hour monitoring window.
D. Lane power variance < 3dB across all transceivers.

Answer: C

Explanation:
CVT identifies marginal 400G DAC links by monitoring the effective bit error rate during validation. An effective BER above the accepted threshold indicates poor signal integrity and points to cables that should be replaced to avoid link instability under cluster workloads.

NEW QUESTION # 141
After upgrading NVIDIA GPU Operator, Kubernetes reports all worker nodes as Ready, but newly created GPU workloads remain in the Pending state. The administrator confirms that GPUs are healthy and visible to the operating system. Which troubleshooting step should be performed first?

A. Disable the Kubernetes scheduler and assign pods manually.
B. Replace every GPU driver with an earlier version immediately.
C. Increase CPU requests for every application pod.
D. Verify that the NVIDIA Device Plugin is running successfully on GPU nodes.

Answer: D

Explanation:
If GPUs are detected by the operating system but Kubernetes cannot schedule GPU workloads, the Device Plugin is one of the first components to verify. It advertises GPU resources to the Kubernetes scheduler. Healthy hardware alone does not guarantee that GPUs are available as schedulable resources within the cluster.

NEW QUESTION # 142
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