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NVIDIA NCP-AII Exam Syllabus Topics:

Section	Weight	Objectives
Deployment and Configuration	25-30%	- Network configuration - System installation and setup - Cluster configuration - Software deployment
Security and Best Practices	10-15%	- Security fundamentals - Compliance considerations - Operational best practices
AI Infrastructure Fundamentals	15-20%	- NVIDIA software stack overview - AI and Deep Learning concepts - GPU architecture basics - Data center infrastructure requirements
Monitoring and Management	15-20%	- Resource utilization - NVIDIA management tools - Performance monitoring - Troubleshooting basics
NVIDIA AI Infrastructure Components	25-30%	- NVIDIA networking solutions (Mellanox) - Storage solutions for AI workloads - NVIDIA AI Enterprise software - NVIDIA DGX systems

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NVIDIA AI Infrastructure Sample Questions (Q129-Q134):

NEW QUESTION # 129
A server with 8 NVIDIAAIOO GPUs is experiencing an unexpected shutdown under heavy load. The IPMI logs show a 'Power Supply Deasserted' event immediately preceding the shutdown. After replacing the PSU, the issue persists. What is the MOST likely cause of the continued shutdowns?

A. Network congestion causing system instability.
B. Insufficient system memory (RAM).
C. A faulty CMOS battery.
D. Incompatible GPU driver version.
E. Overcurrent protection (OCP) tripping due to excessive inrush current during GPU startup.

Answer: E

Explanation:
The 'Power Supply Deasserted' event, even after replacing the PSIJ, strongly suggests that overcurrent protection (OCP) is being triggered. OCP is a safety mechanism that shuts down the PSU if it detects excessive current draw. This is particularly likely with multiple high- power GPUs, as the inrush current during startup can momentarily exceed the PSU's capacity. A driver issue or insufficient memory is less likely to cause this specific event.

NEW QUESTION # 130
After upgrading to HPL-AI 2.0 on a DGX A100 cluster, a 2x performance gain is observed. Which optimization is primarily responsible for this improvement?

A. Automatic NVLink bandwidth doubling via driver updates.
B. Reduction of problem size (N) to accelerate computation.
C. MPI-aware GPU communication that reduces CPU bottlenecks and GPU idle time.
D. Doubting of GPU clock speeds through firmware updates and relevant configuration.

Answer: C

Explanation:
HPL-AI 2.0 improves performance mainly by using MPI-aware GPU communication that allows data movement to occur more efficiently between GPUs. This reduces CPU involvement, lowers communication overhead, and keeps GPUs active instead of waiting on host-side communication bottlenecks.

NEW QUESTION # 131
A BlueField-2 DPU is being used to accelerate storage access via NVMe-oF. You notice high CPU utilization on the host server, despite the DPU. What is the MOST likely cause and the MOST effective initial troubleshooting step?

A. Incorrect NVMe-oF target configuration on the storage array; Check target NQN and discovery settings.
B. The host is still handling a significant portion of the 1/0 processing due to incorrect offload settings; Check the NVMe-oF initiator settings and verify that offload is enabled.
C. Incorrect RoCE configuration; Verify RoCE version and MTU settings using 'roce_util'.
D. Insufficient memory allocated to the BlueField DPIJ; Increase memory allocation via the BMC.
E. Firmware mismatch between the host NIC driver and the BlueField DPU firmware; Upgrade both to the latest compatible versions.

Answer: B

Explanation:
The MOST likely cause of high host CPU utilization with a DPIJ accelerating NVMe-oF is that the host is still handling significant I/O processing. The initial step is to verify NVMe-oF initiator offload settings to ensure the DPIJ is actually handling the workload. Checking target configuration or RoCE, while important, wouldn't directly address high host CPU if the offload isn't working in the first place.

NEW QUESTION # 132
You are deploying a cloud-native AI inference service using Kubernetes and NVIDIA GPUs. You need to ensure that GPU resources are efficiently allocated and monitored. Which of the following approaches is MOST effective for achieving this within the Kubernetes environment?

A. Using the NVIDIA Device Plugin for Kubernetes to advertise GPU resources and utilizing resource requests and limits to schedule pods on nodes with available GPUs.
B. Overcommitting GPU resources and relying on the Kubernetes scheduler to handle potential out-of-memory (OOM) errors.
C. Deploying a dedicated monitoring agent on each node to track GPU utilization and manually adjusting pod resource requests based on these metrics.
D. Relying solely on Kubernetes' default CPU and memory resource requests and limits, assuming GPU usage will be implicitly managed.
E. Manually assigning specific GPU devices to pods using hostPath volumes and environment variables.

Answer: A

Explanation:
The NVIDIA Device Plugin for Kubernetes is specifically designed to advertise GPU resources to the Kubernetes scheduler, enabling efficient allocation and utilization. Resource requests and limits ensure pods are scheduled on nodes with sufficient GPU capacity, preventing resource contention and 00M errors. Options A, C, D, and E are either ineffective, manual, or potentially lead to instability.

NEW QUESTION # 133
You are planning the network infrastructure for a DGX SuperPOD. You need to ensure that the network fabric can handle the high bandwidth and low latency requirements of A1 training workloads. Which network technology is the RECOMMENDED choice for interconnecting the DGX nodes within the SuperPOD, and why?

A. Gigabit Ethernet, because it's widely available and inexpensive.
B. Wi-Fi 6, for wireless connectivity and flexibility.
C. Token Ring, because it's a reliable and deterministic networking protocol.
D. InfiniBand, due to its high bandwidth, low latency, and RDMA support.
E. 10 Gigabit Ethernet, for a balance between cost and performance.

Answer: D

Explanation:
InfiniBand is the recommended network technology for DGX SuperPODs due to its high bandwidth, low latency, and support for RDMA (Remote Direct Memory Access). RDMA allows GPIJs to directly access each other's memory without involving the CPU, significantly reducing latency and improving performance for distributed A1 training workloads. Ethernet, even at higher speeds, generally doesn't offer the same level of performance and RDMA capabilities as InfiniBand.

NEW QUESTION # 134
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