IOT IoT Edge Computing

Question 1

What is the primary driver for adopting edge computing in IoT deployments?

Accepted Answer

Reducing latency and bandwidth by processing data close to where it is generated

Answer

Edge computing reduces the latency and bandwidth costs of sending all IoT data to the cloud by processing and filtering data locally near the source.

Question 2

What is an 'edge node' in an IoT edge computing architecture?

Accepted Answer

A device or gateway at the network edge with local compute capability to process IoT data

Answer

An edge node is a compute-capable device (gateway, server, or ruggedized PC) deployed at or near the IoT data source to process data locally before forwarding to the cloud.

Question 3

Which term describes running machine learning inference on IoT edge devices rather than in the cloud?

Accepted Answer

On-device / Edge AI inference

Answer

Edge AI inference runs trained machine learning models directly on IoT edge devices, enabling real-time decisions without cloud round-trips.

Question 4

What is 'containerization' used for in IoT edge deployments?

Accepted Answer

Packaging edge applications with their dependencies for consistent, portable deployment across diverse edge hardware

Answer

Containerization (e.g., Docker) packages edge applications with all dependencies, enabling consistent deployment across heterogeneous IoT edge hardware without compatibility issues.

Question 5

What challenge does 'heterogeneity' present in IoT edge computing deployments?

Accepted Answer

Managing diverse hardware, operating systems, and communication protocols across a mixed fleet of edge devices

Answer

Heterogeneous IoT edge fleets with different CPUs, OSes, and protocols require specialized management tools to deploy, update, and monitor consistently.

IOT - Internet of Things Practice Test

IOT - Internet of Things Practice Test

IOT IoT Edge Computing