20 Edge Computing Interview Questions and Answers

Edge Computing is a new and upcoming technology that is quickly gaining popularity in the tech industry. With its ability to process data closer to the source, it offers a number of advantages over traditional centralized computing models. As a result, many companies are looking for developers with experience in edge computing.

If you’re interviewing for a position that involves edge computing, it’s important to be prepared to answer questions about your experience and knowledge. In this article, we’ll go over some of the most common edge computing interview questions so you can be ready for your next interview.

Edge Computing Interview Questions and Answers

Here are 20 commonly asked Edge Computing interview questions and answers to prepare you for your interview:

1. What is Edge Computing?

Edge Computing is a type of distributed computing where data processing and storage takes place at the edge of the network, near the data source. This allows for real-time data processing and reduces latency.

2. Can you explain the difference between edge computing and cloud computing?

Edge computing is a type of distributed computing that brings computation and data storage closer to the devices and sensors that are generating the data. This is in contrast to cloud computing, which relies on central servers to store and process data. Edge computing can help to improve response times and reduce bandwidth requirements, as data does not need to be sent to and from a central location.

3. What are some examples of edge devices?

Some examples of edge devices include routers, switches, and firewalls. These devices are often located at the edge of a network, where they can provide security and performance benefits.

4. How do you define the “edge” in edge computing?

The “edge” in edge computing is typically defined as the point where data is first collected, before it is sent to a central location for processing. This can be thought of as the “edge” of the network, as opposed to the core. Edge computing can be used to process data locally, in real-time, without the need to send it back to a central location. This can be beneficial in situations where data needs to be processed quickly, or where there is a need to reduce bandwidth usage.

5. Why is edge computing necessary?

Edge computing is necessary because it allows for data to be processed closer to where it is being generated, which can save time and money. It can also be used to process data in real-time, which is important for many applications such as video streaming and gaming.

6. Is it possible to use edge computing without using cloud services?

Yes, it is possible to use edge computing without using cloud services. However, cloud services can provide a number of advantages to edge computing, such as increased scalability and flexibility.

7. What’s the difference between a fog node and an edge node?

A fog node is a type of edge node that is typically used to provide data processing and storage closer to the edge of the network, where devices are located. An edge node, on the other hand, is a type of node that is typically used to provide data routing and forwarding functionality closer to the edge of the network.

8. What are the benefits of edge computing?

Edge computing can provide a number of benefits, including reduced latency, increased security, and improved efficiency. By moving data processing and storage closer to the edge of the network, edge computing can help to reduce the amount of time that it takes for data to travel back and forth between devices and servers. This can be especially beneficial for applications that require real-time data, such as video streaming or gaming. Additionally, edge computing can help to improve security by keeping data within the confines of a private network. Finally, edge computing can help to improve efficiency by reducing the amount of data that needs to be sent back and forth between devices and servers.

9. What are some disadvantages or challenges associated with edge computing?

One of the main disadvantages of edge computing is that it can be more expensive than traditional cloud computing, since it requires more hardware and infrastructure to be in place. Additionally, edge computing can be less reliable than cloud computing, since it is more susceptible to network outages and other problems.

10. What types of applications can benefit from edge computing?

Any application that requires low latency or that needs to be able to work offline can benefit from edge computing. This includes things like augmented reality, autonomous vehicles, and IoT devices.

11. What are the different models for distributing workloads across edge and cloud resources?

There are three common models for distributing workloads across edge and cloud resources: the public cloud model, the private cloud model, and the hybrid cloud model. In the public cloud model, all workloads are run on resources provided by a public cloud provider. In the private cloud model, all workloads are run on resources provided by a private cloud provider. In the hybrid cloud model, workloads are distributed across both public and private cloud resources.

12. What are some common tools used in edge computing?

Some common tools used in edge computing include:

-Data compression: This is used to reduce the amount of data that needs to be sent back and forth between the edge device and the cloud.

-Caching: This is used to store data locally on the edge device so that it can be accessed more quickly.

-Stream processing: This is used to process data as it is being received, rather than waiting for all of the data to be received before processing it.

13. What are the best practices for designing an edge computing architecture?

There are a few key considerations to keep in mind when designing an edge computing architecture:

1. Make sure to consider the specific needs of your application and use case. Edge computing is often used for applications that require low latency or high throughput, so these should be taken into account when designing your architecture.

2. Make sure to distribute your workloads appropriately. Edge computing architectures often involve distributing workloads across multiple devices, so it is important to consider how these devices will communicate with each other and how to distribute the workloads in a way that is efficient and effective.

3. Make sure to consider security and privacy concerns. Edge computing architectures often involve sensitive data, so it is important to consider how to secure this data and protect the privacy of those who are using the system.

14. What’s your opinion on Kubernetes as an edge computing platform? Do you think that it’s appropriate for managing containerized applications at the edge?

I believe that Kubernetes is a great option for an edge computing platform because it is designed to manage containerized applications at scale. It is also very flexible and can be customized to fit the needs of any particular deployment.

15. What is client-side caching in context with edge computing?

Client-side caching is a type of caching that is done on the client side, as opposed to the server side. In the context of edge computing, client-side caching can be used to help reduce the amount of data that needs to be sent back and forth between the client and the server. By caching data on the client side, the client can avoid having to send requests for data that it already has, which can help reduce latency.

16. How does edge computing relate to IoT?

Edge computing is a type of computing that brings data processing and storage closer to the devices that are generating the data, rather than relying on a centralized server. This can be especially important for IoT devices, which often have limited processing power and bandwidth and thus can benefit from having data processing and storage done locally.

17. How does edge computing affect 5G networking?

5G is the next generation of mobile networks, and it is expected to bring about a new era of edge computing. With 5G, data will be processed closer to the source, which will reduce latency and improve efficiency. This will allow for new applications and services that require real-time data processing, such as augmented reality and virtual reality.

18. How does edge computing impact big data analytics?

Edge computing can help to improve the speed and accuracy of big data analytics by bringing the data processing closer to the data source. This can help to reduce the amount of data that needs to be transmitted over the network, and it can also help to reduce the latency associated with data processing.

19. Where will AI fit into edge computing?

AI will play a big role in edge computing because it will be used to help manage and process the data that is being collected from various devices. Edge computing is all about bringing data processing and storage closer to the devices that are generating the data, and AI will be used to help make sense of all of the data that is being collected.

20. What are some examples of companies already making extensive use of edge computing?

Some companies that are already making use of edge computing include Google, Amazon, and Microsoft. These companies are using edge computing in order to improve the speed and efficiency of their services.