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NVIDIA NCP-AII Prüfungsplan:

Thema	Einzelheiten
Thema 1	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.
Thema 2	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Thema 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.
Thema 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.
Thema 5	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.

>> NCP-AII Übungsmaterialien <<

NCP-AII Deutsche Prüfungsfragen - NCP-AII Ausbildungsressourcen

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NVIDIA AI Infrastructure NCP-AII Prüfungsfragen mit Lösungen (Q156-Q161):

156. Frage
A large A1 model is training using a dataset stored on a network-attached storage (NAS) device. The data transfer speeds are significantly lower than expected. After initial troubleshooting, you discover that the MTU (Maximum Transmission Unit) size on the network interfaces of the training server and the NAS device are mismatched. The server is configured with an MTIJ of 1500, while the NAS device is configured with an MTU of 9000 (Jumbo Frames). What is the MOST likely consequence of this MTU mismatch, and what action should you take?

A. The data transfer will be limited to the lowest common MTU size, but there will be no significant performance impact. No action is required.
B. Data packets will be fragmented, leading to increased overhead and reduced performance. Configure both the server and the NAS device to use the same MTU size (either 1500 or 9000).
C. The server will be unable to communicate with the NAS device. Reduce the MTU size on the server to match the MTU size of the NAS device.
D. The connection between the server and the NAS device will be unreliable, resulting in data corruption. Increase the MTU size on both devices to the maximum supported value.
E. Data packets will be retransmitted, increasing the latency but still getting the full throughput. Configure the server to use Path MTU Discovery (PMTUD).

Antwort: B

Begründung:
An MTU mismatch (option A) will cause fragmentation, where larger packets are broken down into smaller packets before being transmitted, adding overhead and reducing performance. The solution is to configure both devices to use the same MTU size. Choosing 1500 ensures compatibility, while 9000 requires the entire network path to support jumbo frames.

157. Frage
You need to remotely monitor the GPU temperature and utilization of a server without installing any additional software on the server itself. Assuming you have network access to the server's BMC (Baseboard Management Controller), which protocol and standard data format would BEST facilitate this?

A. Syslog with CSV (Comma-separated Values)
B. SNMP (Simple Network Management Protocol) with MIB (Management Information Base)
C. IPMI (Intelligent Platform Management Interface) with SDR (Sensor Data Records)
D. HTTP with JSON
E. SSH with plain text output from 'nvidia-smi'

Antwort: C

Begründung:
IPMI is a standard interface for out-of-band server management, commonly used for monitoring hardware sensors like temperature and utilization. BMCs typically support IPMI. SDRs are the data format used by IPMI for sensor data. SNMP is also an option, but IPMI is more directly tied to hardware monitoring. The rest are less efficient or require additional software installation.

158. Frage
You are managing a server farm of GPU servers used for A1 model training. You observe frequent GPU failures across different servers.
Analysis reveals that the failures often occur during periods of peak ambient temperature in the data center. You can't immediately improve the data center cooling. What are TWO proactive measures you can implement to mitigate these failures without significantly impacting training performance?

A. Increase the fan speeds of the GPU coolers to improve heat dissipation.
B. Schedule training jobs to run during off-peak hours when ambient temperatures are lower.
C. Reduce the GPU power limit using 'nvidia-smi' to decrease heat generation.
D. Replace all existing GPUs with water-cooled models.
E. Implement a more aggressive GPU frequency scaling profile to throttle performance during peak temperatures.

Antwort: B,C

Begründung:
Reducing the GPIJ power limit (A) will directly reduce heat generation, which is the primary driver of failures in this scenario. While it will slightly reduce performance, the impact is less than aggressive throttling. Scheduling training jobs for off-peak hours (D) completely avoids the issue of high ambient temperatures. Increasing fan speeds (B) can help, but it may not be sufficient and can increase noise. Frequency scaling (C) is a more aggressive approach that can significantly impact performance. Replacing all GPUs with water-cooled models (E) is a more expensive and complex solution that may not be immediately feasible.

159. Frage
The system administrator plans to use Multi-Instance GPU profiles. What command should be used to verify that the GPU has this mode enabled?

A. nvidia-mode
B. nvidia-enable
C. nvidia-smi
D. nvidia-mig

Antwort: C

Begründung:
The correct command is nvidia-smi. Multi-Instance GPU, or MIG, is managed and verified through NVIDIA System Management Interface utilities. On supported GPUs such as NVIDIA A100 and H100, nvidia-smi can be used to check whether MIG mode is enabled, list available GPU instances, and confirm how the GPU is partitioned. For example, administrators commonly use commands such as nvidia-smi, nvidia-smi -L, or nvidia-smi -i < GPU_ID > -q to inspect GPU state and MIG information. The options nvidia-mode, nvidia- mig, and nvidia-enable are not standard NVIDIA commands for verifying MIG mode. This is an important server bring-up step because MIG profiles must be confirmed before workloads are scheduled through Kubernetes, Slurm, NVIDIA GPU Operator, or other cluster management tools. If MIG is not enabled or profiles are not created correctly, users may not see the expected GPU slices, and AI inference or multi-tenant workloads can fail placement or run with incorrect resource isolation.

160. Frage
You are configuring a switch port connected to a host in an NCP-AII environment. The host is running RoCEv2. To optimize performance and prevent packet loss, which flow control mechanism should you enable on the switch port?

A. Spanning Tree Protocol (STP).
B. None; flow control is not needed with RoCEv2.
C. Simple Network Management Protocol (SNMP).
D. TCP flow control.
E. Priority Flow Control (PFC) or 802.1 Qbb, specifically for the traffic class associated with RoCEv2.

Antwort: E

Begründung:
Priority Flow Control (PFC), also known as 802.1Qbb, is the appropriate flow control mechanism for RoCEv2. RoCEv2 is a lossless Ethernet protocol, and PFC allows you to enable flow control on specific traffic classes, preventing packet loss in congested situations. By enabling PFC specifically for the traffic class carrying RoCEv2 traffic, you ensure that high-priority A1 training data is delivered reliably without being affected by congestion on other parts of the network.

161. Frage
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