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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA AI Infrastructure (NCP-AII) Certification Exam |
| Exam Number: | NCP-AII |
| Available Languages: | English |
| Exam Duration: | 120 minutes |
| Exam Price: | $400 |
| Exam Format: | Multiple-choice, Scenario-based questions |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 70-75 |
| 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) |
| Recommended Training: | AI Infrastructure & Operations Fundamentals (NVIDIA Training) AI Infrastructure Professional Workshop |
| Exam Registration: | Official NVIDIA Certification Portal NVIDIA AI Infrastructure Certification Page |
| Sample Questions: | NVIDIA NCP-AII Sample Questions |
| Exam Way: | Online proctored exam (remote) or authorized test center depending on region |
| Pre Condition: | Recommended 2–3 years of experience working in data center environments with NVIDIA hardware solutions (GPU servers, networking, storage). |
| Official Syllabus URL: | https://www.nvidia.com/en-eu/learn/certification/ai-infrastructure-professional/ |
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NEW QUESTION # 66
A network engineer is tasked with configuring the management, storage, and compute networks for a new DGX BasePOD deployment. Which statement best describes the network segmentation required for optimal operation?
Answer: A
Explanation:
NVIDIA DGX BasePOD and SuperPOD reference architectures mandate strict network segmentation to ensure performance, security, and manageability.
* Compute Network: Typically InfiniBand (or high-speed Spectrum-X Ethernet), dedicated solely to GPU-to-GPU collective communications (NCCL).
* Storage Network: A high-bandwidth Ethernet or InfiniBand fabric specifically for data ingestion and model checkpointing, often utilizing GPUDirect Storage (GDS).
* Management Network: Used for standard cluster administration, SSH, and software orchestration (e.
g., Bright Cluster Manager or Kubernetes control plane traffic).
* Out-of-Band (OOB) Network: A physically isolated network connected to the BMC ports for low- level system monitoring, power control, and remote console access, even when the OS is down.
A single VLAN (Option A) would cause massive congestion during training, as storage and management traffic would compete with high-frequency compute packets. The four-network model ensures that a "storm" in the storage fabric does not prevent an administrator from accessing the system via the management or OOB networks, which is essential for maintaining an AI Factory at scale.
NEW QUESTION # 67
During HPL execution on a DGX cluster, the benchmark fails with "not enough memory" errors despite sufficient physical RAM. Which HPL.dat parameter adjustment is most effective?
Answer: D
Explanation:
High-Performance Linpack (HPL) is a memory-intensive benchmark that allocates a large portion of available GPU memory to store the matrix $N$. While a server may have 2TB of physical system RAM, the "not enough memory" error usually refers to theHBM (High Bandwidth Memory)on the GPUs themselves. In a DGX H100 system, each GPU has 80GB of HBM3. If the problem size ($N$) specified in the HPL.dat file is too large, the required memory for the matrix will exceed the aggregate capacity of the GPU memory.
Reducing the problem size ($N$) while maintaining the optimal block size ($NB$) ensures that the problem fits within the GPU memory limits while still pushing the computational units to their peak performance.
Increasing the block size (Option C) would actually increase the memory footprint of certain internal buffers, potentially worsening the issue. Reducing $N$ is the standard procedure to stabilize the run during the initial tuning phase of an AI cluster bring-up.
NEW QUESTION # 68
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?
Answer: C
Explanation:
nvidia-smi is the standard NVIDIA command-line utility for checking GPU status, including utilization, temperature, power draw, memory usage, driver state, and running processes. It is the appropriate first command to investigate why a GPU node is performing slower than others.
NEW QUESTION # 69
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: B
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 # 70
After installing the NGC CLI, you attempt to run 'ngc config set' and encounter the following error: 'Error: API key is invalid or missing'.
What are the most likely causes of this issue and how can you resolve them?
Answer: B,C,D
Explanation:
The most likely cause is an invalid API key (B) or a corrupted configuration file (C), or the host lacks network access (E). Reinstalling the package (A) might not resolve the issue if the problem lies with the API key or config file. While NGC service outages (D) are possible, they are less common.
NEW QUESTION # 71
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