Question 1

Your company is running a distributed AI application that involves real-time data ingestion from IoT devices spread across multiple locations. The AI model processing this data requires high throughput and low latency to deliver actionable insights in near real-time. Recently, the application has been experiencing intermittent delays and data loss, leading to decreased accuracy in the AI model's predictions. Which action would BEST improve the performance and reliability of the AI application in this scenario?

Options :

A : Implementing a dedicated, high-bandwidth network link between IoT devices and the data processing system.

B : Switching to a batch processing model to reduce the frequency of data transfers.

C : Deploying a Content Delivery Network (CDN) to cache data closer to the IoT devices.

D : Upgrading the IoT devices to more powerful hardware.

Answer: A

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

Which two components are included in GPU Operator? (Choose two.)

Options :

A : Drivers

B : PyTorch

C : DCGM

D : TensorFlow

Answer: A,C

Question 4

Which statement correctly differentiates between AI, machine learning, and deep learning?

Options :

A : Machine learning is the same as AI, and deep learning is simply a method within AI that doesn't involve machine learning.

B : AI is a broad field encompassing various technologies, including machine learning, which focuses on data-driven models, and deep learning, a subset of machine learning using neural networks.

C : Deep learning is a broader concept than machine learning, which is a specialized form of AI.

D : Machine learning is a type of AI that only uses linear models, while deep learning involves nonlinear models exclusively.

Answer: B

Question 5

Your team is tasked with deploying a new AI-driven application that needs to perform real-time video processing and analytics on high-resolution video streams. The application must analyze multiple video feeds simultaneously to detect and classify objects with minimal latency. Considering the processing demands, which hardware architecture would be the most suitable for this scenario?

Options :

A : Deploy CPUs exclusively for all video processing tasks

B : Deploy GPUs to handle the video processing and analytics

C : Use CPUs for video analytics and GPUs for managing network traffic

D : Deploy a combination of CPUs and FPGAs for video processing

Answer: B

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