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NVIDIA NCP-AII Exam Syllabus Topics:

TopicDetails
Topic 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.
Topic 2
  • Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 3
  • 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.
Topic 4
  • 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.
Topic 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.

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NVIDIA AI Infrastructure Sample Questions (Q131-Q136):

NEW QUESTION # 131
An engineer needs to verify the current firmware versions of all components (ATF, BSP, NIC, UEFI) on a BlueField-3 DPU ' s BMC. Which Redfish API command provides this information?

Answer: B

Explanation:
Modern NVIDIA BlueField DPUs include an integrated Baseboard Management Controller (BMC) that supports the industry-standard Redfish API for out-of-band management. While CLI tools like mlxconfig (Option A) or mstflint (Option C) can be used from the host OS to check the NIC firmware, they cannot easily query the BMC-specific components like the ARM Trusted Firmware (ATF), the Board Support Package (BSP), or the UEFI bootloader of the DPU. The Redfish standard specifies a common URI for hardware inventory. The FirmwareInventory endpoint (Option D) is the correct RESTful path to retrieve a comprehensive JSON object containing the versioning details for all firmware-controllable components on the DPU. This is the preferred method for automated data center management systems (like NVIDIA Base Command Manager) to verify that DPUs are at the correct " Golden Image " version during the staging phase.
Note that " FirmwareList " (Option B) is not a standard Redfish URI for this specific data.


NEW QUESTION # 132
A system administrator wants to configure MIG for seven slices on an H100 GPU in an NVIDIA HGX system. Which command should be used?

Answer: A

Explanation:
The correct answer is mig-parted. NVIDIA MIG Partition Editor, commonly called mig-parted, is designed to automate and manage Multi-Instance GPU partitioning profiles, especially in environments where repeatable MIG layouts are required. An H100 GPU can be divided into multiple isolated GPU instances, and a seven- slice configuration is a common profile pattern for maximizing the number of smaller MIG instances on a single GPU. While nvidia-smi can enable MIG mode and manually create or destroy GPU instances, mig- parted is better suited when an administrator needs to apply defined MIG profiles consistently across HGX systems or multiple nodes. nvcc is the CUDA compiler and has no role in configuring MIG partitions. nvlink- config is not the correct utility for MIG profile configuration. In NVIDIA AI infrastructure, MIG configuration must be controlled carefully because Kubernetes device plugins, GPU Operator, Slurm GRES configuration, and tenant scheduling depend on consistent GPU instance layouts. Using mig-parted helps reduce manual errors and supports repeatable server bring-up for shared inference or multi-tenant AI environments.


NEW QUESTION # 133
You are running a large-scale distributed training job on a cluster of AMD EPYC servers, each equipped with multiple NVIDIAA100 GPUs. You are using Slurm for job scheduling. The training process often fails with NCCL errors related to network connectivity. What steps can you take to improve the reliability of the network communication for NCCL in this environment? Choose the MOST appropriate answers.

Answer: B,D,E

Explanation:
Ensuring network configuration is correct is the most important step. 'srun' with '-mpi=pmi2 handles NCCL environment variable setup by Slurm automatically for proper connectivity. Increasing timeouts allows for transient network issues to resolve without causing failures. Disabling the firewall is a security risk. Decreasing the batch size will reduce the amount of data but won't fix the core network connectivity issues.


NEW QUESTION # 134
Which of the following commands correctly configures the NGC CLI to use a specific API key stored in an environment variable named 'NGC API KEY'?

Answer: A

Explanation:
The 'ngc config set' command uses '-apikey' to specify the API key directly. The correct syntax is 'ngc config set - to ensure the environment variable is properly expanded and passed as a string.


NEW QUESTION # 135
When configuring an out-of-core (OOC) HPL burn-in for a 40B matrix on 8x H100 nodes, which environment variable prevents GPU out-of-memory errors while reserving space for drivers?

Answer: A

Explanation:
HPL_OOC_SAFE_SIZE reserves a safety margin of GPU memory for drivers, runtime overhead, and other allocations during out-of-core HPL execution. Setting it prevents the OOC run from consuming all available GPU memory and helps avoid out-of-memory failures during large matrix burn-in tests.


NEW QUESTION # 136
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