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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	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	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 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	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.

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NVIDIA AI Infrastructure Sample Questions (Q58-Q63):

NEW QUESTION # 58
After installing a new NVIDIA GPU in an AI server, you run 'nvidia-smi' and receive the error 'NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.' You have verified the driver is installed. What is the MOST likely cause?

A. The wrong driver version is installed for the installed CUDA version.
B. The secure boot is enabled preventing the driver from initializing.
C. The system BIOS needs to be updated.
D. The power supply is insufficient to power the GPU.
E. The GPU is not properly seated in the PCIe slot.

Answer: E

Explanation:
A poorly seated GPU often leads to communication failures between the driver and the card. While power, BIOS, and driver versions can be issues, a seating problem is the most common first suspect. Secure boot issues are also possible.

NEW QUESTION # 59
You are tasked with replacing a redundant power supply unit (PSU) in a GPU server. The server has two 2000W PSUs. One PSU has failed, but the server is still running. Which of the following actions is the safest and most efficient way to replace the faulty PSU?

A. Wait for a scheduled maintenance window to power down the server and replace the PSU.
B. Hot-swap the faulty PSU with a new one while the server is running.
C. Replace the faulty PSU, then reboot the server to ensure the new PSU is working.
D. Immediately power down the server and replace the faulty PSIJ.
E. Document the failure and wait until the remaining PSU fails before taking action.

Answer: B

Explanation:
Hot-swapping is the designed method for replacing redundant PSUs in many server systems. This allows the server to remain operational while the faulty PSU is replaced, minimizing downtime. Powering down the server immediately is unnecessary and causes downtime. Waiting for a maintenance window or until the remaining PSU fails increases the risk of a complete server outage.

NEW QUESTION # 60
After installing NGC CLI on RHEL, a user runs ngc registry image list but sees no results. The API key and org are correctly configured. What resolves this?

A. Reinstall the CLI using the yum command instead of manual installation.
B. Run ngc config set --team team-name to specify a team.
C. Ensure the user's NGC account has 'REGISTRY_READ' permissions for the org.
D. Disable SELinux to eliminate unnecessary security restrictions.

Answer: C

Explanation:
If the API key and organization are configured correctly but registry image listings return no results, the account likely lacks the required registry access within that organization. Granting REGISTRY_READ permission allows the NGC CLI to view and list registry images.

NEW QUESTION # 61
You have a large dataset stored on a BeeGFS file system. The training job is single node and uses data augmentation to generate more data on the fly. The data augmentation process is CPU-bound, but you notice that the GPU is underutilized due to the training data not being fed to the GPU fast enough. How can you reduce the load on the CPU and improve the overall training throughput?

A. Decrease the batch size of the training job to reduce the amount of data being processed at each iteration.
B. Increase the number of BeeGFS metadata servers (MDSs) to improve metadata performance.
C. Enable data compression on the BeeGFS file system to reduce the amount of data being transferred over the network.
D. Move the training data to a local NVMe drive on the training node.
E. Implement asynchronous 1/0 in the data loading pipeline using a library like NVIDIA DALI to offload data processing tasks from the CPU to the GPU.

Answer: E

Explanation:
Using NVIDIA DALI (option C) allows you to offload data augmentation and preprocessing tasks from the CPU to the GPU, freeing up CPU resources for other tasks and enabling faster data loading. Moving to a local NVMe drive (A) bypasses BeeGFS but doesn't address the CPU bottleneck. Increasing MDSs (B) improves metadata performance but doesn't directly help with the CPU-bound data augmentation. Decreasing the batch size (D) reduces the workload but doesn't solve the underlying CPU bottleneck. Data compression (E) can increase CPU load due to the decompression process.

NEW QUESTION # 62
After ClusterKit reports "GPU-Host latency exceeds threshold", which NVIDIA diagnostic tool should be used to isolate hardware faults?

A. nvidia-smi topo -m to inspect GPU topology connections
B. ib_write_bw to measure InfiniBand bandwidth between nodes
C. Re-run ClusterKit with --stress=gpu -Y 60 to extend test duration
D. DCGM Diags dcgmi diag -r 2

Answer: D

Explanation:
DCGM Diagnostics with dcgmi diag -r 2 is the NVIDIA hardware diagnostic tool used to isolate GPU-related hardware faults after performance or latency anomalies are detected. It performs deeper GPU health validation beyond topology inspection or workload reruns.

NEW QUESTION # 63
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