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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA-Certified Professional AI Infrastructure Exam |
| Exam Number: | NCP-AII |
| Related Certifications: | NVIDIA-Certified Associate AI Infrastructure and Operations (NCA-AIIO) NVIDIA-Certified Professional AI Operations (NCP-AIO) NVIDIA-Certified Professional AI Networking (NCP-AIN) |
| Passing Score: | Pass/Fail only, no numerical score |
| Exam Duration: | 120 minutes |
| Certificate Validity Period: | 2 years |
| Available Languages: | English |
| Exam Price: | $400 USD |
| Exam Format: | Multiple-choice questions, Scenario-based items |
| Real Exam Qty: | 60-70 |
| Recommended Training: | NVIDIA AI Infrastructure Training |
| Exam Registration: | NVIDIA Certification Portal |
| Sample Questions: | NVIDIA NCP-AII Sample Questions |
| Exam Way: | Online remote proctored or onsite at authorized test centers |
| Pre Condition: | No mandatory prerequisites; recommended 2โ3 years of experience in data center infrastructure, Linux administration, and NVIDIA hardware/software environments |
| Official Syllabus URL: | https://www.nvidia.com/en-us/learn/certification/ai-infrastructure-professional/ |
You can easily download these formats of NVIDIA NCP-AII actual dumps and use them to prepare for the NVIDIA NCP-AII certification test. You don't need to enroll yourself in expensive NCP-AII Exam Training classes. With the NVIDIA NCP-AII valid dumps, you can easily prepare well for the actual NVIDIA NCP-AII exam at home.
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NEW QUESTION # 169
When verifying network cable signal integrity during cluster deployment, which measurement result most strongly indicates a cable signal problem?
Answer: B
Explanation:
The strongest indicator of a cable signal problem is repeated CRC errors combined with intermittent port flapping. CRC errors indicate corrupted frames at the link layer, often caused by poor signal integrity, damaged cables, bad transceivers, dirty optical connectors, excessive bend radius, marginal DAC quality, or unsupported media. Intermittent port flapping means the link repeatedly transitions down and back up, which strongly suggests a physical-layer instability rather than a higher-level application issue. Seeing the expected link speed in ifconfig is not enough to prove signal quality; a link can negotiate at the correct speed while still accumulating errors under load. Successful pings under 2 ms also do not validate high-speed fabric quality because ICMP traffic is lightweight and does not stress the link like RDMA, NCCL, or storage traffic. In NVIDIA AI clusters, physical-layer faults can severely affect distributed training because retransmissions, packet drops, and link resets create stragglers in collective communication. Cable validation should include switch counters, BER or FEC data where available, port stability, transceiver telemetry, and sustained workload or fabric testing.
NEW QUESTION # 170
A system administrator has upgraded the firmware of the DPU. What will be the state of the firmware after the upgrade?
Answer: C
Explanation:
The correct answer is that the firmware is waiting on reboot to become active. For NVIDIA networking devices and DPUs, a firmware upgrade generally writes the new firmware image to the device flash, but the currently running firmware may remain active until the device is reset, rebooted, or power-cycled. NVIDIA MFT documentation notes that loading new firmware may require mlxfwreset, reboot, or power cycle, depending on the device and situation. This distinction is important during server bring-up because administrators must verify both the flashed firmware version and the running firmware version before declaring the update complete. Assuming the firmware is immediately active can lead to version drift across nodes, inconsistent behavior, or failed validation. The firmware is not deleted, and saying it is merely copied but not installed is incomplete because the update process has written it to the device. The operational issue is activation. In production clusters, firmware upgrades should be followed by the required reset procedure and post-update validation using appropriate NVIDIA tools before returning the node to service.
NEW QUESTION # 171
You are configuring a network for a distributed training job using multiple DGX servers connected via InfiniBand. After launching the training job, you observe that the inter-GPU communication is significantly slower than expected, even though 'ibstat' shows all links are up and active. What is the MOST likely cause of this performance bottleneck?
Answer: D
Explanation:
While the other options could contribute to performance issues, the subnet manager (SM) is the MOST likely culprit. A poorly configured or malfunctioning SM can lead to suboptimal path selection (e.g., routing traffic through congested links or longer paths), which significantly increases latency and reduces bandwidth. Congestion control mechanisms, if not properly configured, can also fail to mitigate congestion, leading to packet loss and retransmissions, further degrading performance. Checking the SM logs and configuration is the first step in diagnosing this issue. Incorrect NIJMA placement, small MTIJ, powersave governor, and memory registration limits could also impact performance but are less likely to be the primary bottleneck if the links are up.
NEW QUESTION # 172
An enterprise IT team has completed the physical installation of an AI factory with a Spectrum-X Ethernet network connected to all GPU servers. They now need to ensure the environment is ready for scalable AI workload deployment. What is the recommended sequence of validation steps?
Answer: C
Explanation:
After physical installation, the environment should be validated from the infrastructure layer upward. Confirming firmware and configuration first ensures supported component baselines, link and port checks verify physical connectivity, network benchmarks validate Spectrum-X fabric performance, software stack validation confirms the runtime environment, and staged AI workload tests confirm readiness for scalable production deployment.
NEW QUESTION # 173
After a firmware upgrade on a DGX H100, the administrator notices that one GPU is not detected by the system. Which troubleshooting step should be performed first to identify the root cause?
Answer: A
Explanation:
The first step is to review the firmware update logs and run nvsm show health. After a DGX H100 firmware upgrade, a missing GPU can result from incomplete firmware activation, failed component update, PCIe enumeration failure, GPU tray communication issues, BMC inventory mismatch, or an actual hardware fault.
NVSM is the correct DGX platform-level health tool because it checks hardware state across GPUs, NVSwitch components, PCIe devices, storage, power, cooling, and system sensors. Firmware logs are equally important because they show whether each update completed successfully and whether a reboot, cold power cycle, BMC reset, or AC power cycle is still required. Replacing the GPU immediately is premature and may cause unnecessary downtime. Ignoring the issue is unsafe because production AI workloads expect all GPUs to be visible and healthy. Re-running firmware across all components without diagnosis can hide the original failure or introduce more risk. Proper bring-up practice is to collect evidence, verify hardware health, confirm firmware activation state, and then decide whether reseating, power cycling, reapplying firmware, or service escalation is required.
NEW QUESTION # 174
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