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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA Certified Professional โ AI Infrastructure |
| Exam Number: | NCP-AII |
| Exam Duration: | 90 minutes |
| Available Languages: | English |
| Passing Score: | 700 (scale of 0-1000) |
| Exam Price: | $195 USD |
| Certificate Validity Period: | 2 years |
| Exam Format: | Multiple Choice, Multiple Select |
| Real Exam Qty: | 50 |
| Related Certifications: | NCP-AI NCP-DES |
| Sample Questions: | NVIDIA NCP-AII Sample Questions |
| Exam Way: | Online proctored exam (Pearson VUE) |
| Pre Condition: | Recommended: hands-on experience with NVIDIA AI infrastructure products; basic knowledge of Linux, networking, and data center operations |
| Official Syllabus URL: | https://www.nvidia.com/en-us/certifications/ncp-ai-infra/ |
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NEW QUESTION # 127
An enterprise decides to build a high-performance Ethernet-based AI cluster instead of using InfiniBand. To achieve RDMA functionality with minimal CPU overhead, which technology should the network architect implement?
Answer: B
Explanation:
RoCE (RDMA over Converged Ethernet) enables Remote Direct Memory Access over Ethernet networks, providing low latency and reduced CPU utilization similar to InfiniBand. Proper lossless Ethernet configuration is typically required for optimal performance. VXLAN, MPLS, and GRE provide network virtualization or tunneling rather than RDMA capabilities.
NEW QUESTION # 128
After initial setup and health checks, the DGX H100 system administrator wants to verify that containers can access GPUs before running production workloads. Which method is recommended for this validation?
Answer: B
Explanation:
The definitive "smoke test" for an NVIDIA-accelerated container environment is running nvidia-smi from within a container pulled from theNVIDIA GPU Cloud (NGC). Option D is the only 100% verified command because it includes all three necessary components:
* --gpus all: This flag is required by the NVIDIA Container Toolkit to map the host's GPU device nodes and driver libraries into the container. Without it (as in Option C), the container will not "see" any GPUs.
* nvcr.io/nvidia/cuda: Using an official CUDA base image from NGC ensures that the container has the necessary user-space libraries to communicate with the NVIDIA driver on the host.
* nvidia-smi: This command specifically queries the driver and hardware. If it successfully prints the GPU table (showing the H100/A100 modules, their temperatures, and memory), it proves that the entire stack-from the physical hardware and kernel driver to the container runtime and toolkit-is functioning correctly.
Running ls -la (Option B) or systemctl (Option A) only tests that the container can run, but it does nothing to verify GPU accessibility or driver communication.
NEW QUESTION # 129
You are leading a project to enhance the energy efficiency of a data center that heavily relies on AI workloads. NVIDIA suggests moving beyond traditional metrics like Power Usage Effectiveness (PUE) to better capture the efficiency of modern data centers. Which strategy should you prioritize?
Answer: D
Explanation:
Traditional data center metrics like PUE (Power Usage Effectiveness) only measure how much energy is
"wasted" by cooling and power delivery relative to the IT load; they say nothing about how efficiently that IT load is performing its task. In an AI Factory, "Efficiency" is better defined by the amount of AI training or inference performed per watt. NVIDIA advocates for the use of workload-specific benchmarks, such as MLPerf, to quantify this. MLPerf measures the time and energy required to complete standardized AI tasks (like training a ResNet-50 model or an LLM). By prioritizing these benchmarks (Option C), an organization can compare the energy efficiency of different hardware architectures (e.g., A100 vs. H100) or different software optimizations (e.g., FP8 vs. FP16). For example, even if an H100 system draws more peak power than an older system, its ability to complete a training job 9x faster results in a significantly lower "Total Energy Consumed per Job". This shift from "infrastructure efficiency" (PUE) to "computing efficiency" (MLPerf-per-watt) is essential for modern AI data centers aiming for sustainability and cost-effective scaling.
NEW QUESTION # 130
You are using NVIDIA Spectrum-X switches in your A1 infrastructure. You observe high latency between two GPU servers during a large distributed training job. After analyzing the switch telemetry, you suspect a suboptimal routing path is contributing to the problem. Which of the following methods offers the MOST granular control for influencing traffic flow within the Spectrum-X fabric to mitigate this?
Answer: E
Explanation:
Adaptive Routing (AR) and Dynamic Load Balancing (DLB) are features specifically designed to dynamically adjust paths based on real-time network conditions in Spectrum-X. This provides the most granular and automated way to respond to congestion and optimize traffic flow compared to static routing or global ECMP adjustments. QOS prioritizes, but doesn't change the chosen path.
NEW QUESTION # 131
An infrastructure engineer in an AI factory has successfully replaced a power supply unit on an NVIDIA DGX H100. After installation, both the IN and OUT LEDs on the new power supply illuminate solid green. Which NVSM CLI command should the engineer use to quickly verify the overall system status and ensure it is operating as expected?
Answer: A
Explanation:
nvsm show health provides a quick overall health summary for the DGX system after hardware replacement. It is the appropriate command to confirm that the system is operating normally and that no health issues remain after the PSU installation.
NEW QUESTION # 132
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