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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA AI Infrastructure (NCP-AII) Certification Exam |
| Exam Number: | NCP-AII |
| Exam Price: | $400 |
| Related Certifications: | NVIDIA-Certified Professional AI Operations (NCP-AIO) NVIDIA-Certified Professional AI Networking (NCP-AIN) NVIDIA-Certified Associate AI Infrastructure and Operations (NCA-AIIO) |
| Exam Duration: | 120 minutes |
| Available Languages: | English |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 70-75 |
| Exam Format: | Multiple-choice, Scenario-based questions |
| 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 # 141
An AI cluster needs to transmit data at 200Gbps over a distance of 2km using single-mode fiben Considering cost and performance, which transceiver type is the most appropriate?
Answer: D
Explanation:
200GBASE-LR4 is the most appropriate. 'LR' designates Long Reach, typically up to 10km on single-mode fiber. SR4 is short reach (typically copper or very short fiber). ER4 is Extended Reach (up to 40km). CR4 is copper, for very short distances. DR4 is typically used for up to 500m to 2km distances depending on the specific implementation, so while possible LR4 is a better fit for guaranteed 2km.
NEW QUESTION # 142
An HPC administrator observes that a distributed training workload generates heavy AllReduce traffic across hundreds of GPUs. Although each InfiniBand link is healthy, several links become heavily utilized while others remain nearly idle, limiting overall scalability. Which configuration change is most likely to improve communication efficiency?
Answer: A
Explanation:
Multi-rail networking allows communication libraries such as NCCL to distribute traffic across multiple InfiniBand interfaces, improving bandwidth utilization and reducing congestion. Lowering GPU frequency reduces computational performance without solving the communication imbalance. RDMA should remain enabled for efficient transfers, and NVSwitch is unrelated to inter-server network traffic.
NEW QUESTION # 143
You are deploying a security application that leverages the BlueField DPIJ to perform deep packet inspection (DPI) on network traffic.
Your application requires access to the raw packet data, including the Ethernet headers. Which of the following programming models or APIs is most suitable for accessing raw packet data on the BlueField DPUwith minimal overhead?
Answer: B
Explanation:
DPDK provides a framework for high-performance packet processing in user space, bypassing the kernel's networking stack. The BlueField DPU's PMD (Poll Mode Driver) allows DPDK applications to directly access the network interface with minimal overhead. This is ideal for DPI applications that need to process a large volume of packets at high speed. Standard socket APIs and Netfilter hooks involve kernel intervention, which can introduce significant overhead. Libpcap is a packet capture library, not designed for high-performance packet processing. 'tcpdump' is a command-line tool, not a programming API.
NEW QUESTION # 144
You are troubleshooting an issue where a Docker container utilizing NVIDIA GPUs intermittently fails with a 'CUDA ERROR OUT OF MEMORY error. The host system has sufficient memory and the individual GPU has enough memory as well. You suspect that the problem might be related to how memory is being allocated within the container environment. What steps can you take to investigate and potentially mitigate this issue?
Answer: B,D
Explanation:
A 'CUDA ERROR OUT OF MEMORY' error can occur due to insufficient shared memory within the container (A). Increasing the shared memory size allows the container to allocate more memory for inter-process communication and GPU data transfers. Monitoring GPU memory usage both inside and outside the container (D) is crucial to identify the source of the memory exhaustion. 'CUDA VISIBLE DEVICES' and (B & C) are primarily used for GPU selection and ordering, not memory management, although limiting GPU visibility could indirectly reduce overall memory consumption if the application is poorly designed and tries to allocate memory on all visible GPUs regardless of need. Lowering compute capability won't directly affect memory usage, although the application will need less memory to process, it might cause issues.
NEW QUESTION # 145
Which software library provides GPU acceleration for pandas-like DataFrame operations?
Answer: B
Explanation:
RAPIDS cuDF provides GPU-accelerated DataFrame operations with a pandas-like API, enabling faster data processing on NVIDIA GPUs for analytics and AI workflows.
NEW QUESTION # 146
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