NVIDIA Certified Professional AI Infrastructure (NCP-AII) Practice Questions: Systems and Servers Domain
Test your NVIDIA Certified Professional AI Infrastructure (NCP-AII) knowledge with 10 practice questions from the Systems and Servers domain. Includes detailed explanations and answers.
NVIDIA Certified Professional AI Infrastructure (NCP-AII) Practice Questions
Master the Systems and Servers Domain
Test your knowledge in the Systems and Servers domain with these 10 practice questions. Each question is designed to help you prepare for the NCP-AII certification exam with detailed explanations to reinforce your learning.
Question 1
While deploying vGPU on a server with NVIDIA GPUs, you notice that some VMs are not utilizing the GPU resources effectively. What is the most likely cause of this issue?
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Correct Answer: undefined
Explanation: vGPU profiles must be compatible with the specific GPU model to ensure effective resource utilization. Incompatibility can lead to underutilization or failure to access GPU resources. Option A would prevent GPU passthrough entirely, not just reduce utilization. Option C affects CUDA applications but not vGPU configuration. Option D could cause broader compatibility issues but is less specific to vGPU performance.
Question 2
While monitoring your AI infrastructure with nvidia-smi, you notice frequent GPU temperature spikes. What is the most effective initial step to address this issue?
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Correct Answer: undefined
Explanation: Increasing the fan speed is a direct and immediate way to manage GPU temperatures. While replacing thermal paste and adding air conditioning can help, they are more involved solutions. Reducing workload doesn't address cooling efficiency.
Question 3
You are configuring an NVIDIA DGX server for a new AI project. Which aspect of the physical infrastructure is most critical to ensure system stability and performance?
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Correct Answer: undefined
Explanation: Advanced cooling solutions are crucial for maintaining system stability and performance, especially in high-density GPU environments like DGX servers. Overheating can lead to throttling and hardware failures. While power redundancy and SSDs are important, cooling directly impacts immediate system performance and longevity.
Question 4
In planning the deployment of an NVIDIA DGX system, which factor is most critical when considering power management?
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Correct Answer: undefined
Explanation: Power redundancy is crucial to ensure continuous operation in case of a power failure. While the number of GPUs, cooling efficiency, and cable type are important, redundancy directly affects the system's resilience to power issues.
Question 5
Which of the following is a key consideration when planning power management for a data center with multiple NVIDIA GPU servers?
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Correct Answer: undefined
Explanation: Redundancy and failover capabilities ensure that critical systems remain operational during power failures. Voltage fluctuation tolerance and the number of outlets are important, but redundancy is crucial for reliability.
Question 6
In a multi-node AI training cluster using InfiniBand, which factor is most critical for minimizing latency in data communication?
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Correct Answer: undefined
Explanation: RDMA allows direct memory access from one computer to another without involving the CPU, significantly reducing latency in data transfers over InfiniBand. Increasing GPUs per node (A) and adding switches (C) do not directly address latency, while VLANs (D) are more relevant to network segmentation than latency reduction.
Question 7
During a vGPU deployment, you notice that some virtual machines (VMs) are not utilizing the full memory capacity of the assigned GPU. What could be the reason?
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Correct Answer: undefined
Explanation: vGPU profiles define the amount of GPU memory and compute resources available to VMs. If a profile with limited memory is used, it restricts the VM's access to the GPU's full memory capacity. Option A relates to CPU, not GPU memory. Option C might affect performance but not memory allocation. Option D is unrelated to GPU memory usage.
Question 8
During the deployment of a GPU cluster, you need to ensure minimal latency for AI model training. Which network configuration is most suitable?
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Correct Answer: undefined
Explanation: InfiniBand with RDMA (Remote Direct Memory Access) provides low latency and high throughput, which is ideal for AI model training. Ethernet networks, even with jumbo frames, do not match the latency performance of InfiniBand, and Wi-Fi is not suitable for high-performance computing.
Question 9
When configuring LinkX interconnects for optimal performance in a multi-GPU server, what is the primary factor to consider?
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Correct Answer: undefined
Explanation: Signal attenuation is critical in high-speed interconnects, as it affects data integrity and performance. While connector type and cable length are important, attenuation directly impacts the quality of the signal.
Question 10
When planning the network fabric for a new AI cluster with NVIDIA GPUs, what is the primary consideration for using InfiniBand over Ethernet?
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Correct Answer: undefined
Explanation: InfiniBand is chosen over Ethernet primarily for its lower latency and higher throughput, which are critical for AI workloads requiring fast data transfer between nodes. Option A is incorrect as power consumption is not a primary factor. Option C is incorrect as InfiniBand configuration can be complex. Option D is incorrect as InfiniBand is often more expensive than Ethernet.
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About NVIDIA Certified Professional AI Infrastructure (NCP-AII) Certification
The NCP-AII certification validates your expertise in systems and servers and other critical domains. Our comprehensive practice questions are carefully crafted to mirror the actual exam experience and help you identify knowledge gaps before test day.