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Only need to practice for 20 to 30 hours
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Simulate the real exam
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NVIDIA AI Infrastructure Sample Questions:
1. You are installing multiple NVIDIA GPUs in a server for a deep learning cluster. To optimally utilize the GPUs, which software component(s) are MANDATORY after the physical installation and driver setup? (Select TWO)
A) A deep learning framework like TensorFlow or PyTorch.
B) A text editor.
C) A spreadsheet program.
D) A web browser.
E) NVIDIA CUDA Toolkit.
2. Which of the following statements regarding the benefits of using a BlueField DPU for network offload are TRUE? (Select TWO)
A) Reduced CPU utilization on the host server for network-related tasks.
B) Elimination of the need for a dedicated network interface card (NIC).
C) Increased network throughput due to hardware acceleration.
D) Simplified network configuration compared to traditional NICs.
E) Automatic compatibility with all existing network protocols without requiring software updates.
3. You are configuring a server with NVIDIA GPUs for optimal power efficiency. You want to leverage NVIDIA's power management features to minimize energy consumption during idle periods. Which of the following actions would be the MOST effective in achieving this goal, without significantly impacting performance during active workloads?
A) Remove one or more GPUs from the server to reduce overall power consumption.
B) Enable NVIDIA's Adaptive Clocking and Power Limiting features, allowing the GPU to dynamically adjust its clock speeds and power consumption based on the workload.
C) Reduce the GPU's clock speeds to the lowest possible setting, regardless of workload.
D) Set a very low static power limit for the GPUs, significantly restricting their performance even during active workloads.
E) Disable all GPU power management features to ensure maximum performance at all times.
4. In a distributed training environment with NVLink switches, you need to optimize the data transfer between GPUs on different servers.
Which strategy is most likely to minimize the impact of inter-server latency on the overall training time?
A) Using asynchronous data transfers with overlapping computation.
B) Switching to a synchronous SGD (Stochastic Gradient Descent) algorithm.
C) Using a centralized parameter server architecture.
D) Increasing the batch size to amortize the cost of data transfers.
E) Compressing the data before transferring it over the network.
5. A server with eight NVIDIAAIOO GPUs experiences frequent CUDA errors during large model training. 'nvidia-smi' reports seemingly normal temperatures for all GPUs. However, upon closer inspection using IPMI, the inlet temperature for GPUs 3 and 4 is significantly higher than others. What is the MOST likely cause and the immediate action to take?
A) There is a localized airflow problem affecting GPUs 3 and 4; check fan speeds and airflow obstructions.
B) The temperature sensors on GPUs 3 and 4 are faulty; replace the GPUs immediately.
C) The power supply is failing to provide sufficient power to GPUs 3 and 4; replace the power supply.
D) A driver issue is causing incorrect temperature reporting; reinstall the NVIDIA driver.
E) A software bug in the CUDA toolkit is causing the errors; downgrade to an earlier version.
Solutions:
Question # 1 Answer: A,E | Question # 2 Answer: A,C | Question # 3 Answer: B | Question # 4 Answer: A,D,E | Question # 5 Answer: A |