IOT IoT Edge Computing 1 — Questions and Answers
Question 1: What is the primary driver for adopting edge computing in IoT deployments?
- Reducing device manufacturing costs
- Reducing latency and bandwidth by processing data close to where it is generated (Correct answer)
- Simplifying device hardware design
- Eliminating the need for cloud services entirely
Correct answer: Reducing latency and bandwidth by processing data close to where it is generated
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?
- A sensor with no processing capability
- A device or gateway at the network edge with local compute capability to process IoT data (Correct answer)
- A cloud server in a remote region
- A battery backup unit for IoT devices
Correct answer: A device or gateway at the network edge with local compute capability to process IoT data
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?
- Cloud ML
- Federated learning
- On-device / Edge AI inference (Correct answer)
- Transfer learning
Correct answer: On-device / Edge AI inference
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?
- Physically packaging IoT sensors for shipment
- Packaging edge applications with their dependencies for consistent, portable deployment across diverse edge hardware (Correct answer)
- Encrypting data containers on edge devices
- Managing IoT power supply units
Correct answer: Packaging edge applications with their dependencies for consistent, portable deployment across diverse edge hardware
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?
- Too much available computing power at the edge
- Managing diverse hardware, operating systems, and communication protocols across a mixed fleet of edge devices (Correct answer)
- Excessive network bandwidth at the edge
- Standardized device programming making updates difficult
Correct answer: Managing diverse hardware, operating systems, and communication protocols across a mixed fleet of edge devices
Heterogeneous IoT edge fleets with different CPUs, OSes, and protocols require specialized management tools to deploy, update, and monitor consistently.
Question 6: What is 'edge orchestration' in IoT deployments?
- Coordinating music playback on smart speakers
- Automated management of workload deployment, scaling, and lifecycle across distributed IoT edge nodes (Correct answer)
- Physical installation of edge hardware
- Coordinating time synchronization on edge devices
Correct answer: Automated management of workload deployment, scaling, and lifecycle across distributed IoT edge nodes
Edge orchestration automates the deployment, scaling, and management of applications across distributed IoT edge nodes, similar to how Kubernetes manages cloud workloads.
IOT IoT Edge Computing 1
What is the primary driver for adopting edge computing in IoT deployments?