Free NCP-AII Exam Braindumps
AI Infrastructure Questions 26–30 (page: 7)

Updated On: 19-Sep-2026

A systems engineer is updating firmware across a large DGX cluster using automation.
What is the best practice for minimizing risk and ensuring cluster health during and after the process?

  1. Drain nodes from the scheduler, run pre-update diagnostics, update firmware in batches, and verify health post-update before scaling to the next batch.
  2. To save time, simultaneously update all nodes in the cluster without draining or diagnostics.
  3. Update nodes that have reported faults, leaving others on older firmware.
  4. Drain nodes from the scheduler, update firmware in batches, skip diagnostics and verify health post-update before scaling to the next batch.

Answer(s): A

Explanation:

Updating firmware on an NVIDIA DGX cluster is a critical operation that involves multiple sensitive components, including the GPU baseboard, the BMC, the motherboard tray (SBC), and the InfiniBand HCAs. In a production environment, "Batching" is the industry standard to prevent a single corrupted firmware image or update failure from taking down the entire AI factory. The process must begin with "Draining" the nodes in the workload scheduler (like Slurm or Kubernetes) to ensure no active training jobs are interrupted. Running pre-update diagnostics—using tools like nvsm show health or dcgmi diag—is vital to establish a baseline and ensure the hardware is stable before applying changes. Once the firmware is applied in a controlled batch, post-update verification is required to confirm the system returns to a "Healthy" state and that all versions match the target manifest. This "Rolling Update" strategy allows the engineer to pause the automation if a specific node fails to return to service, protecting the overall availability of the cluster. Skipping diagnostics (Option D) or leaving nodes on mismatched versions (Option C) creates "configuration drift," which leads to unpredictable performance in collective communication libraries.



One of the nodes in a cluster is not running as fast as the others and the system administrator needs to check the status of the GPUs on that system.
What command should be used?

  1. lspci | grep NVIDIA
  2. nvidia-smi
  3. nvidia-gpu-status
  4. iblinkinfo

Answer(s): B

Explanation:

The nvidia-smi (NVIDIA System Management Interface) utility is the primary tool for monitoring and managing the state of NVIDIA GPUs.
When a node exhibits "jitter" or lower performance compared to its peers in a cluster, nvidia-smi provides the necessary granular data to identify the bottleneck. It reports critical metrics such as GPU utilization percentages, memory usage, and, most importantly, "Clocks Throttle Reasons." If a GPU is running slower due to power capping, thermal issues, or a hardware error (like an uncorrectable ECC error), nvidia-smi will display this state immediately.
While lspci (Option A) can confirm if the GPU is physically visible on the PCIe bus, it cannot provide any telemetry regarding its operational performance or health. iblinkinfo (Option D) is a network-level tool that only monitors InfiniBand link states. In an AI infrastructure context, nvidia-smi is the first-line diagnostic to determine if a GPU is "healthy" and operating at its intended clock speeds.



A company has a registered NGC account and their server has NGC CLI installed.
What step should be taken first to gain access to NGC?

  1. ngc config get
  2. ngc init
  3. ngc config set
  4. ngc config update

Answer(s): C

Explanation:

The NVIDIA GPU Cloud (NGC) is the central repository for AI-optimized containers, pre-trained models, and specialized SDKs. To interact with the NGC registry via the command line, the ngc CLI must be authenticated to the user's account. The command ngc config set is the verified first step to configure these credentials.
When this command is executed, the user is prompted to provide their API Key, which is generated from the NGC web portal. This configuration process creates a local config file (typically in ~/.ngc/config) that stores the authentication token, the preferred organization, and the team settings. Without running ngc config set, the CLI cannot authenticate requests to pull private containers or upload models. ngc init (Option B) is not a standard configuration command for the current NGC CLI architecture, and ngc config get (Option A) is only useful for viewing an existing configuration that has already been established.



A user encounters "permission denied" errors when running GPU-accelerated containers on a Secure
Boot-enabled system.
What resolves this?

  1. Enroll the MOK and sign NVIDIA kernel modules.
  2. Reinstall Docker without the NVIDIA runtime.
  3. Disable SELinux to relax unnecessary security policies.
  4. Run Docker with sudo for elevated privileges.

Answer(s): A

Explanation:

On systems where UEFI Secure Boot is enabled, the Linux kernel strictly enforces a "Signature Verification" policy for all loaded kernel modules. The NVIDIA driver consists of several modules (like nvidia.ko and nvidia-uvm.ko) that provide the interface between the hardware and the NVIDIA Container Runtime. If these modules are not signed by a key trusted by the system's firmware, the kernel will block them from loading, leading to "Permission Denied" errors when the container attempts to access /dev/nvidiactl or the GPU device nodes. To resolve this without compromising the security of the host, the administrator must utilize a Machine Owner Key (MOK). The modules must be signed using this key, and the key itself must be "Enrolled" into the system's Secure Boot database via the MOK management interface during the boot process. This establishes a "Chain of Trust" that allows the drivers to function. Disabling SELinux (Option C) or using sudo (Option D) will not resolve this, as the blockage is happening at the kernel-firmware interface level, not at the user-space permission level.



An engineer needs to validate NVLink Switch functionality on a DGX H100 system with 8 GPUs.
Which NCCL command verifies intra-node NVLink bandwidth?

  1. broadcast_perf -b 8 -e 16G -f 2 -g 8 without split configuration
  2. all_reduce_perf -b 8 -e 16G -f 2 -g 4 with NCCL_TESTS_SPLIT="MOD 2"
  3. all_reduce_perf -b 8 -e 16G -f 2 -g 1 repeated 8 times
  4. all_reduce_perf -b 8 -e 16G -f 2 -g 8 with NCCL_TESTS_SPLIT="OR 0x7"

Answer(s): D

Explanation:

The NVIDIA Collective Communications Library (NCCL) "Tests" are used to verify the maximum achievable bandwidth of the interconnects. On a DGX H100, the GPUs are connected via a dedicated high-bandwidth NVLink Switch fabric (NVLink 4), which provides significantly higher throughput than PCIe. To validate the intra-node (within a single server) performance, the all_reduce_perf test is used. The command in Option D is specifically designed to stress all 8 GPUs (-g 8) across a wide range of message sizes (8 bytes to 16G). The use of the environment variable NCCL_TESTS_SPLIT with the bitwise "OR" or "AND" masks allows the engineer to isolate specific traffic patterns or groups of GPUs to ensure the NVLink switches are distributing the load evenly. For a standard 8-GPU H100 tray, achieving a "Bus Bandwidth" of ~450 GB/s to 900 GB/s (depending on the precision and message size) confirms that the NVLink fabric is operating at its theoretical peak. Using only 4 GPUs (Option B) or 1 GPU (Option C) would not provide a complete picture of the NVLink switch bisection bandwidth.



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!