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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA AI Infrastructure (NCP-AII) Certification Exam |
| Exam Number: | NCP-AII |
| Real Exam Qty: | 70-75 |
| Exam Price: | $400 |
| Related Certifications: | NVIDIA-Certified Professional AI Networking (NCP-AIN) NVIDIA-Certified Associate AI Infrastructure and Operations (NCA-AIIO) NVIDIA-Certified Professional AI Operations (NCP-AIO) |
| Available Languages: | English |
| Exam Duration: | 120 minutes |
| Exam Format: | Scenario-based questions, Multiple-choice |
| Certificate Validity Period: | 2 years |
| Recommended Training: | AI Infrastructure & Operations Fundamentals (NVIDIA Training) AI Infrastructure Professional Workshop |
| 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 # 75
You are tasked with upgrading the NVIDIA driver on a Kubernetes node hosting GPU-accelerated A1 workloads. To minimize downtime and ensure a smooth transition, which sequence of steps should you follow?
Answer: D
Explanation:
Cordoning the node prevents new pods from being scheduled on it. After upgrading the driver and rebooting, uncordoning the node allows Kubernetes to resume scheduling workloads. Draining the node before upgrading can cause unnecessary downtime if pods are migrated before the upgrade process starts. The NVIDIA container toolkit must be compatible to the NVIDIA driver, but the upgrade sequence follows Option C steps.
NEW QUESTION # 76
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: C
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 # 77
During a 48-hour NeMo question-answering model burn-in test, GPU memory errors occur when processing large datasets. Which configuration strategy prevents Out-of-Memory (OOM) errors while maintaining processing efficiency?
Answer: C
Explanation:
NVIDIA NeMo and large language model (LLM) training workloads are extremely demanding onHBM (High Bandwidth Memory). Out-of-Memory (OOM) errors often occur not because the total dataset is too large, but because memory fragmentation or sudden spikes in allocation (spikes during data shuffling or batch loading) exceed the available GPU memory. To mitigate this during intensive burn-in tests, engineers utilize theRMM (RAPIDS Memory Manager)library, which provides an asynchronous allocator. Enabling RMM asynchronous allocation allows the system to pre-allocate a pool of memory and manage it more efficiently than the standard CUDA allocator, reducing the overhead of constant allocations and deallocations.
Furthermore, setting a specific blocksize (e.g., 1GB) for data loading ensures that the data ingestion pipeline reads data in manageable, deterministic chunks. This prevents the system from attempting to load massive files entirely into memory at once, which is the primary cause of OOMs in question-answering tasks involving large Parquet or JSON datasets. Switching to FP32 (Option B) would actually double the memory footprint and increase the likelihood of an OOM error.
NEW QUESTION # 78
You are responsible for ensuring interoperability between AI applications deployed across a diverse IT landscape, including an on-premises data center equipped with NVIDIA GPUs and multiple cloud platforms from different vendors. These environments need to support complex AI workflows that involve large-scale data processing, real-time analytics, and machine learning model training. To maintain consistent performance and flexibility, which strategy should you prioritize?
Answer: C
Explanation:
The best strategy is to ensure compatible storage protocols and APIs across the on-premises and cloud environments. AI workflows often move through multiple stages, including data ingestion, preprocessing, training, checkpointing, validation, inference, and analytics. If each environment uses incompatible storage interfaces, applications may require custom integration work, data copies, or workflow redesign. Using common protocols and APIs such as NFS for file-based access or S3-compatible APIs for object access improves portability and allows AI tools, data pipelines, and orchestration systems to operate more consistently across platforms. Standardizing on one vendor may reduce management complexity, but it can limit flexibility and create lock-in. Using only native cloud storage with middleware can work, but it may add complexity and inconsistent performance. Increasing network bandwidth helps data movement, but it does not solve protocol compatibility or application integration. In NVIDIA AI infrastructure, storage design must support high-throughput data access while also preserving operational flexibility across DGX, HGX, Kubernetes, Slurm, and cloud-connected AI environments.
NEW QUESTION # 79
An administrator needs to manually deploy the BlueField image on a target DPU. The administrator downloads the new image file and needs to flash it to the hardware. Which command should the administrator use?
Answer: D
Explanation:
bfb-install --rshim is used to manually install or flash a BlueField boot image to a target DPU through the RShim interface. This is the appropriate method when deploying a downloaded BlueField image directly to the hardware.
NEW QUESTION # 80
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