Your AI team is deploying a real-time video processing application that leverages deep learning models across a distributed system with multiple GPUs. However, the application faces frequent latency spikes and inconsistent frame processing times, especially when scaling across different nodes. Upon review, you find that the network bandwidth between nodes is becoming a bottleneck, leading to these performance issues.
Which strategy would most effectively reduce latency and stabilize frame processing times in this distributed AI application?

• A.Increase the number of GPUs per node
• B.Reduce the video resolution to lower the data load
• C.Optimize the deep learning models for lower complexity
• D.Implement data compression techniques for inter-node communication

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You are managing a high-performance AI cluster where multiple deep learning jobs are scheduled to run concurrently. To maximize resource efficiency, which of the following strategies should youuse to allocate GPU resources across the cluster?

• A.Use a priority queue to assign GPUs to jobs based on their deadline, ensuring the most time-sensitive jobs complete first.
• B.Allocate all GPUs to the largest job to ensure its rapid completion, then proceed with smaller jobs.
• C.Allocate GPUs to jobs based on their compute intensity, reserving the most powerful GPUs for the most demanding tasks.
• D.Assign jobs to GPUs based on their geographic proximity to reduce data transfer times.

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You are assisting a professional administrator in ensuring data integrity during AI model training in an AI data center. Which of the following strategies would best contribute to maintaining data integrity across distributed GPU nodes?

• A.Use a single master node with GPUs to manage all data processing and then distribute the results to other nodes
• B.Assign data verification tasks to DPUs, allowing GPUs to focus solely on model training
• C.Utilize redundant GPU nodes to independently process data and compare results post-training
• D.Implement a distributed file system with replication, ensuring that each GPU node has access to the same consistent dataset

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You are managing an AI infrastructure that includes multiple NVIDIA GPUs across various virtual machines (VMs) in a cloud environment. One of the VMs is consistently underperforming compared to others, even though it has the same GPU allocation and is running similar workloads.What is the most likely cause of the underperformance in this virtual machine?

• A.Misconfigured GPU passthrough settings
• B.Inadequate storage I/O performance
• C.Insufficient CPU allocation for the VM
• D.Incorrect GPU driver version installed

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As a junior team member, you are tasked with running data analysis on a large dataset using NVIDIA RAPIDS under the supervision of a senior engineer. The senior engineer advises you to ensure that the GPU resources are effectively utilized to speed up the data processing tasks. What is the best approach to ensure efficient use of GPU resources during your data analysis tasks?

• A.Disable GPU acceleration to avoid potential compatibility issues
• B.Use CPU-based pandas for all DataFrame operations
• C.Focus on using only CPU cores for parallel processing
• D.Use cuDF to accelerate DataFrame operations

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