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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA-Certified Professional AI Infrastructure Exam |
| Exam Number: | NCP-AII |
| Related Certifications: | NVIDIA-Certified Professional AI Networking (NCP-AIN) NVIDIA-Certified Professional AI Operations (NCP-AIO) NVIDIA-Certified Associate AI Infrastructure and Operations (NCA-AIIO) |
| Real Exam Qty: | 60-70 |
| Certificate Validity Period: | 2 years |
| Available Languages: | English |
| Exam Format: | Multiple-choice questions, Scenario-based items |
| Passing Score: | Pass/Fail only, no numerical score |
| Exam Price: | $400 USD |
| Exam Duration: | 120 minutes |
| 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 2–3 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/ |
>> Latest NCP-AII Test Questions <<
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NEW QUESTION # 123
Which of the following steps are essential components of a recommended DGX cluster installation procedure?
Pick the 2 correct responses below.
Answer: C,D
Explanation:
The correct essential steps are grouping nodes by function and validating networking on every node. In DGX cluster deployments, nodes are commonly organized by role, such as head nodes, compute nodes, login nodes, storage nodes, or management components. Cluster management tools such as NVIDIA Base Command Manager use these logical groupings or categories to apply software images, configurations, monitoring, and operational actions consistently. NVIDIA DGX SuperPOD documentation describes cluster manager concepts where devices represent components such as head nodes, physical nodes, switches, and PDUs, and older DGX SuperPOD guidance references default BCM node categories for login and compute systems. Network validation is equally important before higher-level software deployment because InfiniBand, Ethernet, management, and storage interfaces must be correctly cabled, addressed, and reachable. Installing Slurm before compute node images are correctly prepared reverses the expected foundation-first workflow. Skipping node health or storage validation is unsafe because distributed workloads depend on consistent GPU health, network reachability, and storage access. A reliable DGX cluster installation starts with structured node roles, validated networks, consistent images, and then workload orchestration and application testing.
NEW QUESTION # 124
An AI infrastructure team upgrades a training cluster from PCIe-only GPU servers to HGX systems with NVLink and NVSwitch. Single-GPU benchmark results remain unchanged, but multi-GPU training jobs complete significantly faster. Which workload characteristic most directly benefits from the new architecture?
Answer: D
Explanation:
NVLink and NVSwitch primarily improve communication between GPUs within the same server.
Workloads involving frequent gradient synchronization, tensor exchange, or collective operations benefit from the substantially higher bandwidth and lower latency compared to PCIe. CPU preprocessing, storage performance, and Kubernetes scheduling are largely unaffected by the GPU interconnect architecture.
NEW QUESTION # 125
You are configuring a server with multiple GPUs for CUDA-aware MPI. Which environment variable is critical for ensuring proper GPU affinity, so that each MPI process uses the correct GPU?
Answer: D
Explanation:
'CUDA VISIBLE DEVICES' is essential for GPU affinity. It allows you to specify which GPUs are visible to a particular process. Without it, all processes might try to use the same GPU, leading to performance bottlenecks. controls the order in which GPUs are enumerated. specifies the path to shared libraries. is hypothetical. forces synchronous CUDA calls.
NEW QUESTION # 126
Why is it important to provide a large and high-performance local cache (using SSDs configured as RAID-0) for deep learning workloads on DGX systems?
Answer: D
Explanation:
Deep learning training involves iterating over a dataset many times (epochs). If a 32-node cluster pulls the same dataset from a central NFS storage server for every epoch, the network and storage fabric quickly become a bottleneck due to "Incast" traffic. By using the high-speed NVMe drives internal to a DGX system (configured inRAID-0for maximum performance, not redundancy), the system can implement a local cache.
During the first epoch, data is pulled from the remote storage and simultaneously written to the local SSDs.
For all subsequent epochs, the training framework reads the data directly from the local RAID-0 array. This significantly reduces NFS trafficand network congestion, allowing the training to proceed at the full speed of the local NVMe storage ($25\text{ GB/s}+$ on modern DGX systems). Option C is incorrect because RAID-0 provides no redundancy; if a drive fails, the cache is lost, but since it is just a cache, the data still exists on the primary storage. Option B refers to GPUDirect Storage, which is a separate technology from local RAID-0 caching.
NEW QUESTION # 127
A DGX A100 server with dual power supplies reports a critical power event in the BMC logs. One PSU shows a 'degraded' status, while the other appears normal. What immediate actions should you take to ensure continued operation and prevent data loss?
Answer: A,C
Explanation:
Hot-swapping the degraded PSU (B) restores redundancy. Migrating workloads (E) minimizes the risk of data loss or service interruption if the remaining PSU fails. Shutting down the server (A) causes unnecessary downtime if hot-swapping is possible. Monitoring the remaining PSU (C) is a good practice, but it's not a replacement for restoring redundancy or mitigating risk. Reducing GPU power limits (D) may help prevent further strain but is a temporary solution that impacts performance.
NEW QUESTION # 128
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