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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA AI Infrastructure (NCP-AII) Certification Exam |
| Exam Number: | NCP-AII |
| Certificate Validity Period: | 2 years |
| Related Certifications: | NVIDIA-Certified Associate AI Infrastructure and Operations (NCA-AIIO) NVIDIA-Certified Professional AI Networking (NCP-AIN) NVIDIA-Certified Professional AI Operations (NCP-AIO) |
| Exam Price: | $400 |
| Real Exam Qty: | 70-75 |
| Exam Duration: | 120 minutes |
| Exam Format: | Scenario-based questions, Multiple-choice |
| Available Languages: | English |
| Recommended Training: | AI Infrastructure Professional Workshop AI Infrastructure & Operations Fundamentals (NVIDIA Training) |
| Exam Registration: | NVIDIA AI Infrastructure Certification Page Official NVIDIA Certification Portal |
| 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 # 112
A financial services firm is deploying an AI model for fraud detection that requires rapid inference and data retrieval across multiple sites. Which feature should their storage system prioritize?
Answer: D
Explanation:
The storage system should prioritize multi-protocol data access with low latency. Fraud detection workloads often depend on near-real-time inference, rapid lookup of transaction history, feature retrieval, and integration across multiple systems or sites. In an NVIDIA AI infrastructure environment, storage must support the AI workflow without starving GPUs, inference servers, or analytics pipelines. Multi-protocol access allows different applications and environments to access data through suitable interfaces, such as file or object protocols, while maintaining interoperability across on-premises and cloud-connected platforms. Low latency is essential because fraud decisions are time-sensitive; delayed data access can reduce model usefulness or prevent immediate action. Tape backup systems are useful for archival retention but are not suitable for live inference or fast analytics. Low-cost HDD-only storage may provide capacity but usually cannot meet latency and throughput requirements. High capacity with moderate speed is also insufficient when the business requirement is rapid retrieval across sites. For production AI operations, storage should be designed for responsiveness, availability, protocol compatibility, and consistent performance under concurrent workload demand.
NEW QUESTION # 113
You are an infrastructure engineer tasked with validating a new AI training cluster before releasing it to users.
Your team wants to perform a NeMo burn-in to ensure both hardware and software are reliable and ready for production workloads. Which of the following actions are required as part of a proper NeMo burn-in process?
Pick the 2 correct responses below.
Answer: A,C
Explanation:
A proper NeMo burn-in should use a representative training or pretraining workload and run it for a sustained period across the intended GPUs and nodes. The goal is not only to prove that NeMo can import or that a model can return an inference result. The goal is to exercise the full AI infrastructure path: GPUs, CPU memory, CUDA, NCCL, network fabric, storage, container runtime, scheduler integration, and framework stack. Configuring a representative NeMo recipe and executor ensures the job launches with realistic distributed-training behavior and uses the same type of resources expected in production. Running the job repeatedly or for an extended duration helps expose intermittent issues such as NCCL stalls, GPU Xid errors, storage slowdowns, thermal throttling, node instability, or performance regression. A quick accuracy check on a pre-trained model is not a burn-in because it does not adequately stress distributed training. Inference-only testing is also insufficient for validating a training cluster. NeMo burn-in is valuable because it validates both the software framework and the underlying NVIDIA AI infrastructure under realistic workload pressure.
NEW QUESTION # 114
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: B
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 # 115
Consider this scenario. You have a containerized A1 application that requires specific CUDA libraries. You want to manage the deployment and scaling of this application across multiple Kubernetes clusters, some of which might have different versions of NVIDIA drivers installed. How would you handle the CUDA dependency management in this multi-cluster environment to ensure compatibility and reproducibility?
Answer: D
Explanation:
Packaging the CUDA libraries into the container image ensures consistent behavior across all clusters, regardless of the driver version installed on each cluster's nodes. It is the most portable solution. Options B and C introduce runtime dependencies and increase complexity. Option D becomes unwieldy to manage. Option E is highly discouraged as the image can be updated without your knowledge.
NEW QUESTION # 116
A system administrator needs to validate a GPU-based server and ensure that no errors occur under load.
What command should be used?
Answer: B
Explanation:
While there are many ways to stress a system, the verified method to check for errors under load in an NVIDIA DGX environment is to monitor the system usingNVSM (NVIDIA System Management). Running nvsm show health is the standard command to verify that all hardware components-including GPUs, memory, and storage-are operating within their defined specifications. To truly ensure no errors occur
"under load," an administrator will typically run a workload (like HPL or NCCL tests) and concurrently run nvsm show health or nvsm monitor to check for real-time telemetry such as thermal throttling, PCIe errors, or power fluctuations. nvsm show health aggregates data from the BMC and the OS to provide a "Red/Green" status of the entire system. There is no standard command named nvsm stress-test (Option D) or stress-test -- usage (Option B) in the official NVIDIA DGX software stack.
NEW QUESTION # 117
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