NCP-AII Fragen&Antworten, NCP-AII Probesfragen

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NVIDIA NCP-AII Prüfungsplan:

Thema	Einzelheiten
Thema 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.
Thema 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.
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	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.
Thema 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 NCP-AII Prüfungsfragen mit Lösungen (Q69-Q74):

69. Frage
You have an NVIDIAAIOO GPU and need to configure it for optimal performance across two distinct AI workloads: a large language model (LLM) training job and a computer vision inference service. The LLM benefits from maximum memory bandwidth, while the inference service requires low latency and high throughput. Which MIG configuration would best suit this scenario?

A. Create two 7g.80gb MIG instances, one for each workload.
B. Create one log. 120gb instance for the LLM and one 4g.40gb instance for inference.
C. Create one 14g.160gb MIG instance for the LLM and use CUDA MPS to multiplex the inference service.
D. Utilize Time-Slicing on a single full-GPU instance, allocating specific time slots to each workload using NVIDIA Vgpu technology
E. Create a single full-GPU instance and use Kubernetes resource quotas to isolate the workloads.

Antwort: B

Begründung:
Creating a log. 120gb instance for the memory-intensive LLM and a 4g.40gb instance for the inference service provides dedicated resources that cater to the specific needs of each workload, without the overhead or limitations of CUDA MPS or Kubernetes resource quotas. Option A is too conservative, potentially limiting the LLM performance. Option B sacrifices dedicated resources for inference, which may hurt latency. Option C does not leverage MIG and does not guarantee resource isolation and performance consistency. Option E introduces complexities associated with Time-Slicing and might not be suitable for real-time processing.

70. Frage
A cluster administrator needs to validate transceiver firmware versions across 200 ports using UFM. Which GUI-based method provides a consolidated view?

A. Export all switch logs and grep for 'FW Version " .
B. Run mlxlink -d lid- < LID > -m on each port manually.
C. Use " Topology' view to visually inspect cable icons.
D. Navigate to 'Devices " > select a switch > " Cables ' tab to see ASIC firmware and transceiver versions.

Antwort: D

Begründung:
Managing a large-scale AI fabric requires centralized visibility into the physical layer. The NVIDIA Unified Fabric Manager (UFM) provides a comprehensive Dashboard for InfiniBand networks. To check transceiver firmware-which is critical for ensuring feature parity and stability across the fabric-the administrator can use the UFM Enterprise GUI. By navigating to the " Devices " section and selecting a specific switch, the " Cables " tab will aggregate telemetry for every occupied port. This view displays the manufacturer, part number, and the specific firmware version of the transceivers (LinkX) or Active Optical Cables (AOC). This consolidated view is far more efficient than manual CLI queries (Option C) for 200+ ports. Maintaining uniform firmware across transceivers ensures that optimizations like Adaptive Routing and Congestion Control perform consistently across the entire 400G or 200G fabric.

71. Frage
You are experiencing link flapping (frequent up/down transitions) on several InfiniBand links in your AI infrastructure. This is causing intermittent connectivity issues and performance degradation. What are the MOST likely causes of this issue, and what steps should you take to troubleshoot and resolve it? (Select TWO)

A. Software bugs in the operating system or InfiniBand drivers.
B. Faulty or damaged cables, connectors, or transceivers.
C. Mismatched link speeds or duplex settings between connected devices.
D. Excessive broadcast traffic causing congestion.
E. Incorrect MTU (Maximum Transmission Unit) configuration on the affected interfaces.

Antwort: B,C

Begründung:
Link flapping is most commonly caused by physical layer issues (faulty cables, connectors, or transceivers) or configuration mismatches (link speeds or duplex settings). Troubleshooting should focus on inspecting the physical connections and verifying that the link speed and duplex settings are correctly configured on both ends of the link. While MTIJ issues and software bugs can cause network problems, they are less likely to directly cause link flapping. Excessive broadcast traffic can cause performance issues but is less likely to result in frequent link up/down transitions.

72. Frage
A team is validating a DGX BasePOD deployment. Using cmsh, they run a command to check GPU health across all nodes. What indicates that the system is ready for AI workloads?

A. At least half of the GPUs report Status_Health = OK
B. Only the head node's GPUs need to be healthy.
C. The command output is ignored if the system powers on without errors.
D. All GPUs report Status_Health = OK and Health = OK for each device.

Antwort: D

Begründung:
A DGX BasePOD is ready for AI workloads only when GPU health checks pass across the cluster. Seeing Status_Health = OK and Health = OK for every GPU confirms that all GPU devices are detected, healthy, and available for workload scheduling.

73. Frage
You are deploying a security application that leverages the BlueField DPIJ to perform deep packet inspection (DPI) on network traffic.
Your application requires access to the raw packet data, including the Ethernet headers. Which of the following programming models or APIs is most suitable for accessing raw packet data on the BlueField DPUwith minimal overhead?

A. Using the 'tcpdump' command and parsing its output.
B. using DPDK (Data Plane Development Kit) with the BlueField DPU's PMD (Poll Mode Driver).
C. Using libpcap to capture packets from the network interface.
D. Using Netfilter hooks in the Linux kernel to intercept packets.
E. Using standard Linux socket APIs (e.g., 'socket()' , 'recvfrom()') in user space.

Antwort: B

Begründung:
DPDK provides a framework for high-performance packet processing in user space, bypassing the kernel's networking stack. The BlueField DPU's PMD (Poll Mode Driver) allows DPDK applications to directly access the network interface with minimal overhead. This is ideal for DPI applications that need to process a large volume of packets at high speed. Standard socket APIs and Netfilter hooks involve kernel intervention, which can introduce significant overhead. Libpcap is a packet capture library, not designed for high-performance packet processing. 'tcpdump' is a command-line tool, not a programming API.

74. Frage
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