NVIDIA-Certified Professional: AI Infrastructure Practice Questions: Systems and Networks Domain
Test your NVIDIA-Certified Professional: AI Infrastructure knowledge with 10 practice questions from the Systems and Networks domain. Includes detailed explanations and answers.
NVIDIA-Certified Professional: AI Infrastructure Practice Questions
Master the Systems and Networks Domain
Test your knowledge in the Systems and Networks domain with these 10 practice questions. Each question is designed to help you prepare for the NVIDIA-Certified Professional: AI Infrastructure certification exam with detailed explanations to reinforce your learning.
Question 1
While monitoring the performance of an AI workload, you observe unexpected latency in data processing. Which nvidia-smi metric would be most useful to identify potential bottlenecks?
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Correct Answer: B
Explanation: Memory usage is critical for identifying bottlenecks related to data transfer and processing. High memory usage can indicate insufficient memory bandwidth or capacity, leading to latency. Fan speed, temperature, and power consumption are less directly related to processing latency.
Question 2
While monitoring an NVIDIA DGX system using nvidia-smi, you notice frequent power limit throttling. What is the most effective action to resolve this?
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Correct Answer: A
Explanation: Increasing the power supply capacity addresses power limit throttling by ensuring the system can provide sufficient power to all components. Reducing GPU count, disabling ECC, or lowering temperature thresholds do not solve power supply issues.
Question 3
Which nvidia-smi command option is most useful for identifying GPU memory bottlenecks during AI model training?
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Correct Answer: A
Explanation: The --query-gpu=utilization.memory option provides information on the percentage of memory utilization, which is crucial for identifying memory bottlenecks. Options B, C, and D provide information on temperature, power, and fan speed, which are not directly related to memory usage.
Question 4
In planning the network fabric for a new AI infrastructure deployment using NVIDIA GPUs, what is a critical consideration to ensure scalability and performance?
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Correct Answer: C
Explanation: High-speed interconnects and redundancy are essential for scalability and performance, ensuring that the network can handle increased loads and provide reliable service. Cheap switches (A) may lack necessary features, a flat topology (B) can limit scalability, and limiting capacity (D) can quickly lead to bottlenecks.
Question 5
You are tasked with optimizing the performance of an NVIDIA DGX A100 system in a data center. The system is connected to an InfiniBand network. Which of the following actions would most effectively reduce latency in data transfers between nodes?
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Correct Answer: B
Explanation: Enabling adaptive routing on the InfiniBand fabric allows the network to dynamically choose the best path for data packets, reducing congestion and latency. Increasing MTU size (A) can help with throughput but not necessarily latency. Reducing the number of subnets (C) does not directly affect latency. Switching to Ethernet (D) would likely increase latency compared to InfiniBand.
Question 6
What is the primary purpose of GPU Direct technology?
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Correct Answer: B
Explanation: GPU Direct technology enables direct memory access between GPUs and other devices (like network adapters or storage) without going through system memory, reducing latency and improving performance in data-intensive applications.
Question 7
What is the recommended approach to manage LinkX interconnects in a high-performance NVIDIA AI infrastructure?
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Correct Answer: A
Explanation: Regularly updating the firmware of LinkX cables ensures compatibility and performance optimizations. While cable length should be minimized, the type of cable (copper vs. optical) should be chosen based on specific performance needs, and manual routing is typically less efficient than automated solutions.
Question 8
When configuring NVIDIA Collective Communications Library (NCCL) for multi-node training, which network topology typically provides the best performance?
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Correct Answer: C
Explanation: Fat-tree topology with rail-optimized paths provides the best performance for NCCL communications by offering multiple high-bandwidth paths between nodes and optimizing communication patterns for collective operations like AllReduce.
Question 9
What is the maximum number of vGPU instances that can typically be created on a single NVIDIA A100 GPU?
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Correct Answer: D
Explanation: A single NVIDIA A100 GPU can support up to 64 vGPU instances when using the smallest vGPU profiles (like A100-1-5C), though practical deployments often use fewer instances with larger profiles depending on workload requirements.
Question 10
In a scenario where nvidia-smi reports lower than expected GPU utilization on a DGX server, which of the following is the best first step in troubleshooting?
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Correct Answer: B
Explanation: Checking for CPU bottlenecks is the best first step because CPU limitations can restrict GPU performance. Rebooting or updating drivers should be considered after ruling out resource bottlenecks. Running diagnostics is more time-consuming and should be done if simpler checks don't resolve the issue.
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About NVIDIA-Certified Professional: AI Infrastructure Certification
The NVIDIA-Certified Professional: AI Infrastructure certification validates your expertise in systems and networks 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.
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