NVIDIA NCP-AII Exam Prep
AI Infrastructure (Page 7 )

Updated On: 17-Sep-2026

What command sequence is used to identify the exact name of the server that runs as the master SM in a multi-node fabric?

  1. sminfo, then smpquery ND
  2. ibstat, then sminfo
  3. ibnetdiscover, then ibsim
  4. sminfo, then smpquery NI

Answer(s): A

Explanation:

In an InfiniBand fabric, the Subnet Manager (SM) is the "brain" of the network, responsible for discovering the topology, assigning Local Identifiers (LIDs), and calculating routing tables. In a multi-node fabric, there is typically one Master SM and several Standby SMs for high availability. To identify the master, the sminfo command is first used; it queries the fabric and returns the LID of the current Master SM. Once the LID is obtained, the engineer must map that numerical LID to a physical server name or Node Description. The smpquery ND (Node Description) command is then executed, targeting that specific LID. This sequence is vital for troubleshooting fabric-wide issues, as logs on the Master SM server provide the definitive record of sweeps, traps, and topology changes. Using smpquery NI (Node Info) would provide hardware-level details like the GUID and device ID, but it does not return the human-readable string (server name) defined in the Node Description, which is necessary for rapid identification in a crowded data center.



You are following the official steps to install the NVIDIA Container Toolkit using a package manager on Ubuntu. After importing the NVIDIA package repository and GPG key, what is the next action?

  1. Reboot the host system to apply the repository changes and proceed.
  2. Install the nvidia-container-toolkit package using your package manager.
  3. Format the disk to clear any existing NVIDIA-related dependencies first.
  4. Download the CUDA toolkit installer from NVIDIA'S official website.

Answer(s): B

Explanation:

The NVIDIA Container Toolkit (formerly nvidia-docker2) is the essential middleware that allows Docker, Podman, or Containerd to "see" and utilize the host's GPU hardware. The standard installation workflow on Debian-based systems like Ubuntu involves three core phases: repository configuration, package installation, and runtime configuration. Once the GPG key is added (to ensure package integrity) and the .list file is placed in /etc/apt/sources.list.d/ (to point to the NVIDIA production servers), the local package index must be refreshed via apt-get update. Immediately following this, the administrator must install the toolkit using the command sudo apt-get install -y nvidia-container-toolkit. Rebooting (Option A) is unnecessary at this stage because no kernel modules have been modified yet. Downloading the CUDA Toolkit (Option D) is a separate step; notably, the Container Toolkit allows containers to run CUDA applications even if the host only has the NVIDIA driver installed, making the driver—not the host CUDA toolkit—the primary prerequisite.



What information does the 'ibnodes' command display?

  1. All hosts & switches
  2. All host & server names
  3. All server names
  4. All channel adapters

Answer(s): A

Explanation:

The ibnodes utility is a specialized script within the infiniband-diags suite used for high-level topology mapping.
When executed, it scans the InfiniBand subnet and provides a concise list of every active node connected to the fabric, categorized by their type. Specifically, it displays all Host Channel Adapters (HCAs/CA) found in the servers (the "hosts") and all InfiniBand switches. This command is an essential first step in fabric validation after a "bring-up" or expansion because it allows an engineer to quickly verify if the expected number of nodes and switches have successfully joined the fabric. Unlike ibnetdiscover, which provides a verbose and complex output of every port-to-port connection, ibnodes focuses on the identity and type of the devices. This distinction is critical for troubleshooting connectivity; if a server's HCA is missing from the ibnodes list, the engineer knows to investigate the physical link or the local HCA driver state rather than searching through thousands of lines of switch-to-switch cabling data.



During cluster validation, the Cable Validation Tool (CVT) reports "Underperforming (BER)" for an InfiniBand link.
Which BER thresholds indicate a critical signal quality issue requiring cable replacement?

  1. Rx power variance > 3dB between lanes
  2. Effective BER > 0 during the first 125 minutes of link operation
  3. Raw BER > 1e-12 or Effective BER > 1.5E-254 for <6hr measurements
  4. Temperature > 85°C on transceiver module

Answer(s): C

Explanation:

NVIDIA's Cable Validation Tool (CVT) and the Unified Fabric Manager (UFM) use strict Bit Error Rate (BER) thresholds to ensure the stability of NDR (400G) and HDR (200G) InfiniBand fabrics. Because modern high-speed links rely on Forward Error Correction (FEC) to fix minor bit flips, a "Raw BER" (errors before FEC) is expected, but must remain within a specific envelope—typically better than $10^{-12}$. However, the "Effective BER" (errors after FEC) should ideally be zero or incredibly low (less than $1.5 \times 10^{-254}$) over a long observation window. If these thresholds are exceeded within a standard 6-hour monitoring period, it indicates that the signal-to-noise ratio is too low for the FEC to maintain a reliable stream. This leads to packet drops and "Symbol Errors" that trigger InfiniBand "Retransmissions," which are catastrophic for the performance of AI collectives like all_reduce.
While Rx power variance (Option A) and temperature (Option D) are health indicators, they are causes of poor BER, not the BER threshold itself.



A 24-hour HPL burn-in fails with "illegal value" errors during the first iteration.
Which initial troubleshooting step resolves this without compromising burn-in validity?

  1. Switch from FP64 to FP32 precision.
  2. Disable GPU affinity.
  3. Reduce test duration to 12 hours.
  4. Verify the matrix size is divisible by block size.

Answer(s): D

Explanation:

High-Performance Linpack (HPL) is the standard benchmark for stress-testing the computational stability and thermal endurance of an AI cluster. It solves a massive dense system of linear equations, and its mathematical configuration is highly sensitive. The HPL.dat configuration file defines the Problem Size ($N$) and the Block Size ($NB$). A fundamental requirement of the HPL algorithm is that the workload must be distributed evenly across the MPI processes and GPU threads. If the total matrix size $N$ is not an exact multiple of the block size $NB$, or if the grid dimensions ($P \times Q$) do not align with the hardware topology, the solver may encounter an "illegal value" error or a "residual too large" failure at the very beginning of the run. This is a configuration error, not a hardware fault. Reducing the precision (Option A) would invalidate the test, as HPL must run in FP64 to be considered a standard "burn-in." Verifying that $N$ is divisible by $NB$ ensures the mathematical integrity of the test while allowing the hardware to be pushed to its theoretical performance limits.



Viewing page 7 of 16
Viewing questions 31 - 35 out of 170 questions


Post your Comments and Discuss NVIDIA NCP-AII exam prep with other Community members:

AI Tutor AI Tutor 👋 I’m here to help!