AI-Enabled Edge Computing Quiz

Edge computing enhances security by processing data closer to its source, minimizing exposure to potential breaches during transmission.

AI-enabled edge computing offers improved response times and facilitates real-time decision-making for IoT devices.

One challenge is the limited computational resources available on edge devices, which can hinder the execution of complex AI algorithms.

<p>Processing personal data on edge devices raises concerns regarding data privacy and potential unauthorized access over insecure networks.</p> Signup and view all the answers

<p>Federated Learning contributes to performance by enabling local training of machine learning models on edge devices, thereby improving efficiency and reducing the need for extensive data transmission.</p> Signup and view all the answers

<p>Model aggregation in Federated Learning involves collecting and combining the updates from local models to create a more accurate global model.</p> Signup and view all the answers

<p>Reduced bandwidth requirements in edge computing lower data transmission costs and alleviate network congestion.</p> Signup and view all the answers

<p>Deep learning can enhance edge computing applications by enabling more accurate image and speech recognition capabilities.</p> Signup and view all the answers

<p>A primary benefit is reduced latency, allowing for real-time data analysis and decision-making at the edge.</p> Signup and view all the answers

<p>It provides significant computing power needed to analyze large amounts of data and train complex AI models.</p> Signup and view all the answers

<p>Cloud deployment is centralized and ideal for applications needing high scalability, while edge computing is distributed and suited for real-time processing.</p> Signup and view all the answers

<p>Cloud computing allows for scalable infrastructure, enabling easy expansion as IoT systems grow in complexity.</p> Signup and view all the answers

<p>Two common cloud service models are Infrastructure as a Service (IaaS) and Platform as a Service (PaaS).</p> Signup and view all the answers

<p>Cloud computing reduces the need for physical hardware, minimizing maintenance costs and allowing users to pay only for the resources they use.</p> Signup and view all the answers

<p>AWS IoT Analytics provides tools for analyzing data collected from IoT devices, enabling businesses to gain insights and improve operations.</p> Signup and view all the answers

<p>Data privacy concerns arise as centralized control over data access can lead to vulnerabilities if not managed properly.</p> Signup and view all the answers

<p>Security concerns include data breaches, unauthorized access, and vulnerabilities in device communications.</p> Signup and view all the answers

<p>Cloud-based data analytics services provide pre-built AI functionalities and big data analytics capabilities.</p> Signup and view all the answers

<p>Edge computing enhances security by processing data locally, reducing the amount of sensitive information transmitted to the cloud.</p> Signup and view all the answers

<p>A hybrid approach combines private and public cloud resources, optimizing usage based on specific application needs.</p> Signup and view all the answers

<p>Microsoft Azure IoT Hub focuses on advanced analytics and edge computing, while AWS IoT Core emphasizes supported device management and real-time analytics.</p> Signup and view all the answers

<p>By offloading processing and storage requirements to the cloud, it frees up local device resources to focus on primary functions.</p> Signup and view all the answers

<p>Real-time analytics allows for immediate insights and responses to data, facilitating timely decisions that can enhance operational efficiency.</p> Signup and view all the answers

<p>Latency caused by the distance to data centers can negatively impact the performance of IoT applications.</p> Signup and view all the answers