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NVIDIA NCP-AII Exam Syllabus Topics:

Topic	Details
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	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 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	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.

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NVIDIA AI Infrastructure Sample Questions (Q78-Q83):

NEW QUESTION # 78
Which of the following is the MOST critical consideration when planning the cooling strategy for a server rack containing multiple NVIDIA A100 GPUs?

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

Answer: C

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 # 79
After a firmware upgrade on a DGX H100, the administrator notices that one GPU is not detected by the system. Which troubleshooting step should be performed first to identify the root cause?

A. Ignore the issue and proceed with production workloads if the other GPUs are operational.
B. Immediately re-run the firmware upgrade on all system components.
C. Remove the GPU from the system and replace it with a new one before any diagnostics.
D. Review firmware update logs and run nvsm show health to check for hardware or firmware errors on the affected GPU.

Answer: D

Explanation:
The first step is to review the firmware update logs and run nvsm show health. After a DGX H100 firmware upgrade, a missing GPU can result from incomplete firmware activation, failed component update, PCIe enumeration failure, GPU tray communication issues, BMC inventory mismatch, or an actual hardware fault.
NVSM is the correct DGX platform-level health tool because it checks hardware state across GPUs, NVSwitch components, PCIe devices, storage, power, cooling, and system sensors. Firmware logs are equally important because they show whether each update completed successfully and whether a reboot, cold power cycle, BMC reset, or AC power cycle is still required. Replacing the GPU immediately is premature and may cause unnecessary downtime. Ignoring the issue is unsafe because production AI workloads expect all GPUs to be visible and healthy. Re-running firmware across all components without diagnosis can hide the original failure or introduce more risk. Proper bring-up practice is to collect evidence, verify hardware health, confirm firmware activation state, and then decide whether reseating, power cycling, reapplying firmware, or service escalation is required.

NEW QUESTION # 80
You are monitoring a server with 8 GPUs used for deep learning training. You observe that one of the GPUs reports a significantly lower utilization rate compared to the others, even though the workload is designed to distribute evenly. 'nvidia-smi' reports a persistent "XID 13" error for that GPU. What is the most likely cause?

A. A hardware fault within the GPU, such as a memory error or core failure.
B. Insufficient system memory preventing data transfer to that GPU.
C. The GPU's compute mode is set to 'Exclusive Process'.
D. A driver bug causing incorrect workload distribution.
E. An incorrect CUDA version installed.

Answer: A

Explanation:
XID 13 errors in 'nvidia-smi' typically indicate a hardware fault within the GPU. Driver bugs or memory issues would likely cause different error codes or system instability across multiple GPUs. CUDA version mismatch might prevent the application from running altogether, but is less likely to lead to a specific XID error on a single GPU. Exclusive Process mode will lead to it being used by a different process but not necessarily cause that XID error.

NEW QUESTION # 81
After replacing a faulty NVIDIA GPU, the system boots, and 'nvidia-smi' detects the new card. However, when you run a CUDA program, it fails with the error "'no CUDA-capable device is detected'". You've confirmed the correct drivers are installed and the GPU is properly seated. What's the most probable cause of this issue?

A. The 'LD LIBRARY PATH* environment variable is not set correctly.
B. The new GPU is incompatible with the existing system BIOS.
C. The CUDA toolkit is not properly configured to use the new GPU.
D. The GPIJ is not properly initialized by the system due to a missing or incorrect ACPI configuration.
E. The user running the CUDA program does not have the necessary permissions to access the GPU.

Answer: D

Explanation:
The error "no CUDA-capable device is detected", even when 'nvidia-smi' sees the GPIJ, points to a lower-level system issue that prevents CUDA from accessing the card. In such scenarios, ACPI (Advanced Configuration and Power Interface) misconfiguration is frequently the culprit. ACPI handles device initialization and power management. If ACPI doesn't properly configure the new GPU, CUDA programs won't be able to access it. Checking and correcting ACPI configuration would be the first line of action, which includes ensuring proper settings in the system BIOS/IJEFI related to PCI devices, especially those related to GPU/accelerators. LD LIBRARY PATH would affect runtime linking of CUDA libraries, but not the base device detection. User permissions are less likely to be the cause since 'nvidia-smr works.

NEW QUESTION # 82
You notice that one of the fans in your GPU server is running at a significantly higher RPM than the others, even under minimal load. ipmitool sensor' output shows a normal temperature for that GPU. What could be the potential causes?

A. A network connectivity issue is causing higher CPU utilization, leading to increased system-wide heat.
B. The fan is attempting to compensate for restricted airflow due to dust buildup.
C. The server's BMC (Baseboard Management Controller) has a faulty temperature sensor reading, causing it to overcompensate.
D. The fan bearing is wearing out, causing increased friction and requiring higher RPM to maintain airflow.
E. The fan's PWM control signal is malfunctioning, causing it to run at full speed.

Answer: B,D,E

Explanation:
A malfunctioning PWM control signal, worn fan bearings, or restricted airflow can all cause a fan to run at higher RPMs. While a faulty BMC sensor could be a cause, the question states that 'ipmitool sensor' shows a normal temperature. Network connectivity issues are less likely to cause an isolated fan to run high, if the GPU temperature is normal.

NEW QUESTION # 83
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