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NVIDIA NCP-AII Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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.
Topic 3
  • Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 4
  • 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 5
  • 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.

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NVIDIA AI Infrastructure Sample Questions (Q59-Q64):

NEW QUESTION # 59
An infrastructure engineer runs an NCCL burn-in on an eight-node GPU cluster. Over a 12-hour period, all GPUs are tested with repeated all-reduce collectives. Monitoring tools show the following observations:
Aggregate bandwidth remains within 5% of documented reference for the hardware on every run.
No errors or timeouts are reported in NCCL logs.
On three occasions, one GPU logged single-run bandwidth dips of 15-20% compared to its normal performance, but performance recovered on the next run and stayed stable afterward. System logs show no hardware or driver errors.
Two minor NCCL WARN-level messages about "unexpected latency spike" appear in system logs for separate nodes, but could not be reproduced.
Which conclusion is the best strategy before releasing the cluster to production?

Answer: B

Explanation:
The best conclusion is to proceed, because the cluster met sustained bandwidth expectations, reported no NCCL errors or timeouts, and showed no persistent hardware, driver, or fabric faults. In NVIDIA AI infrastructure validation, burn-in testing is intended to detect repeatable failures, degraded links, unstable GPUs, NCCL communication errors, timeouts, or sustained performance below reference values. Short, unreproducible latency or bandwidth variation can occur because of transient system activity, monitoring overhead, scheduler noise, background services, or brief congestion. Since aggregate bandwidth stayed within
5% of the documented reference on every run and the dips recovered immediately without recurring on the same component, the evidence does not justify declaring the burn-in failed. Option B is too strict because one unreproduced transient dip is not enough to prove hardware failure. Option C is also excessive because excluding nodes without repeatable evidence reduces cluster capacity unnecessarily. The correct operational strategy is to approve the cluster while preserving logs, documenting the anomalies, and continuing normal monitoring during early production workloads.


NEW QUESTION # 60
A user needs to configure NGC CLI to access resources across multiple organizations. What is the recommended command syntax to achieve this?

Answer: C

Explanation:
The correct answer is ngc config set --org org-name --ace ace-name. NGC CLI uses configuration context to determine which organization, team, or entitlement scope the user is operating in. When users belong to multiple organizations, the CLI must be configured with the intended organization context so that registry access, model downloads, private containers, and enterprise software resources resolve correctly. NVIDIA documentation shows ngc config set as the command used to configure or switch NGC context, including organization and team values, and other NVIDIA installation workflows show NGC configuration prompting for organization, team, and ACE fields. Manually editing JSON configuration files is not the recommended operational method because it increases the risk of syntax errors and inconsistent authentication state. ngc registry login is not the normal command for setting persistent NGC CLI organization context. Exporting an environment variable may influence a session, but it is not the best persistent configuration method. In AI infrastructure operations, correct NGC configuration is important for pulling validated containers, GPU drivers, vGPU software, models, and enterprise assets from the correct account scope.


NEW QUESTION # 61
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?

Answer: C

Explanation:
CVT marks a link as critically underperforming when the raw BER exceeds the acceptable physical-layer threshold or when the effective BER exceeds the defined long-window threshold.
These values indicate a signal integrity problem severe enough to require cable inspection and likely replacement.


NEW QUESTION # 62
You are tasked with configuring a BlueField-2 DPIJ to offload network virtualization functions. Which Mellanox OFED command is MOST crucial for verifying that SR-IOV is correctly enabled and functional on the host and the DPU, prior to configuring vPorts?

Answer: E

Explanation:
"Ispci -vv' is the most crucial command. It shows the presence of Virtual Functions (VFs) which confirms SR-IOV is enabled. It also shows the device IDs assigned, allowing verification that the host and DPIJ are correctly seeing and assigning VFs to the network interfaces. While other commands are useful for general network diagnostics, they don't directly verify SR-IOV functionality like 'Ispci -vv' does.


NEW QUESTION # 63
Which of the following steps are essential components of a recommended DGX cluster installation procedure? (Choose two.)

Answer: C,D

Explanation:
A recommended DGX cluster installation requires validating network interfaces and fabric connectivity before deploying cluster software so node provisioning and workload communication are reliable. Grouping nodes by function and assigning them to the proper cluster management categories ensures consistent configuration, management, and role-based deployment across the cluster.


NEW QUESTION # 64
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