Question 1

You are responsible for deploying a deep learning model for image recognition across a global network of autonomous vehicles. The model requires real-time inference and must be updated frequently with new training data. The deployment needs to be highly efficient in both edge and cloud environments, with minimal latency. Which combination of NVIDIA technologies would best meet the requirements for this deployment?

Options :

A : Use NVIDIA Quadro RTX GPUs on each vehicle for real-time inference.

B : Deploy the model on NVIDIA DGX systems located in data centers for real-time inference.

C : Use NVIDIA Jetson AGX Xavier for edge inference and NVIDIA Fleet Command for centralized management.

D : Utilize NVIDIA Tesla V100 GPUs in the cloud with TensorRT for optimized inference.

Answer: C

Question 2

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?

Options :

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

Answer: D

Question 3

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?

Options :

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

Answer: D

Question 4

You are planning to deploy a large-scale AI training job in the cloud using NVIDIA GPUs. Which of the following factors is most crucial to optimize both cost and performance for your deployment?

Options :

A : Selecting instances with the highest available GPU core count

B : Enabling autoscaling to dynamically allocate resources based on workload demand

C : Ensuring data locality by choosing cloud regions closest to your data sources

D : Using reserved instances instead of on-demand instances

Answer: B

Question 5

You are tasked with optimizing the performance of a deep learning model used for image recognition. The model needs to process a large dataset as quickly as possible while maintaining high accuracy. You have access to both GPU and CPU resources. Which two statements best describe why GPUs are more suitable than CPUs for this task? (Select two)

Options :

A : CPUs are better suited for handling the large dataset due to their superior memory bandwidth.

B : GPUs have a higher number of cores compared to CPUs, allowing for parallel processing of many operations simultaneously.

C : GPUs are optimized for matrix operations, which are common in deep learning algorithms.

D : GPUs have a lower latency than CPUs, making them faster for individual calculations.

E : CPUs consume less power than GPUs, making them more suitable for prolonged computations

Answer: B,C

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