Top 5 Interview-Winning Edge Computing Questions

This article was published as a part of the Data Science Blogathon.

Introduction

In this constantly growing technical era, everybody wants faster and smarter computing that provides usage information. Edge computing is a kind of networking philosophy that brings processing capabilities closer to the end-user or the source of data to decrease latency and bandwidth use.

In practice, edge computing implies having less computation and storage in the cloud and instead moving those processes to local places, like on a user’s personal computer, an IoT device, or an edge server. This relocation towards the network’s edge reduces the communication gap that has to happen between a client and server.

If you’re applying for an edge computing role, you should be prepared to answer questions regarding your expertise and knowledge. In this blog, I have discussed five interview-winning questions that help you to set a pace and ace your upcoming interview! So let’s begin.

Q1:- How Can We Define the Term “Edge” in Edge Computing?

Ans:- Whenever we discuss the definition of edge computing, our focus can be where we collect the data. The term “edge” basically specifies the point where data is collected primarily before passing to a central location for processing.

Below are the possible components of the edge:-

Edge devices: Edge devices are physical hardware placed in remote locations at the network’s edge. A huge number of edge devices are already available in the market to collect and process data locally without touching the physical world, for example, Smart watches, smart speakers, smartphones, etc. Also, we have many edge devices that can compute data locally and talk to the cloud, like the Internet of Things (IoT) devices, point of sales (POS) systems, vehicles, robots, and sensors.

On-premises infrastructure: On-premise infrastructure is used for the management of local systems and establishing their connection with the networks and could be servers, bridges, routers, hubs, or containers.

Network edge: There is no explicit requirement for a separate “edge network”, but when a separate network is involved, this is just another location in the continuum between customers and the cloud. This is where 5G can come into the role.

5G can connect highly powerful wireless connectivity with high cellular speed and low latency, bringing fascinating opportunities like remote telesurgery, autonomous drones, smart city, smart home projects, and much more.

The role of network edge is instrumental in scenarios that are too expensive and difficult to put computing on-premises. Yet, high responsiveness is needed, i.e., the cloud is too distant.

Q2:- What is the Need for Edge Computing?

Ans:- Below are some points which define why we need edge computing in today’s world:-

Increase in Bandwidth:- It enhances the efficient usage of bandwidth by analyzing the data at the edges itself, as opposed to the cloud, which needs transfer of data from the IoT devices requiring high bandwidth, proving it beneficial for applications in remote locations with minimum cost.

Enable smart applications: Quick response is the demand of critical business and self-driving automobiles; it enables smart applications and devices to react to data simultaneously.

Security:- It can process the data without putting it on a public cloud, ensuring complete data security.

Reliability:- While on an extended network, data might get corrupted, thus affecting the data reliability for the companies to use.

Limitation to Cloud:- The data computation at the edge limits the utilization of cloud computing.

Q3:- How is Edge Computing Related to IoT?

Ans:- Edge computing brings data processing closer to the devices where data is collected or used instead of relying on a centralized server. This allows the IoT data to be collected and processed at the edge instead of sending the data back to the cloud. This can benefit IoT devices by having data processing and storage done locally.

Source:- https://geekflare.com/edge-computing-and-its-applications/

IoT and edge computing can combine to rapidly analyze data in real-time as the involvement of edge in IoT can increase performance and security by reducing latency and bandwidth use.

Q4:- What is the Difference Between Edge Computing And Cloud Computing?

Ans:- The purpose of Edge Computing is not to replace cloud computing technology; the appearance somehow impacts cloud computing. On the other side, we can say that technology will enhance cloud computing by providing easy solutions for handling huge data. Although they both have their purpose and use, here we have discussed how they are different from each other:-

Source:-https://towardsdatascience.com/you-need-to-move-from-cloud-computing-to-edge-computing-now-e8759eb9690f

Q5:- Explain Two Real-world Applications Of Edge Computing.

Ans:- Autonomous Vehicles: Autonomous vehicles or self-driving cars are trying to become the new normal in the market. In this competitive period, the data generated by self-driving cars, with 100s of on-vehicle sensors, is around 40 terabytes/4000GBs for every eight hours of driving. Here comes the role of edge computing, as the data is huge, and it is impractical and unsafe to send all the data to the cloud. It limits the data to the cloud so that the car can respond immediately to the events with valuable data.

Edge computing is a set of computing storage, data analysis, networking technologies, and data management which allows instantaneous action to the data by the applications and devices.

Autonomous vehicles are connected to the edge to enhance efficiency, improve safety, decrease traffic congestion, and reduce accidents.

Source:- https://www.softeq.com/blog/edge-computing-and-iot-potential-benefits-and-practical-use-cases

Fleet Management: Fleet owners are facilitated with huge opportunities by Edge Computing technology. Let’s take some examples:-

• It could predict breakdowns, i.e., any truck can immediately inform its driver if any part of the truck is going to fail. This prediction is based on the individual data of the truck, the driver’s behavior, and the driving conditions.
• It can detect distracted driving or driver fatigue by analyzing the driving patterns from the available video, like drifting across lanes.
• It can help alert drivers when they are following a too close to a vehicle, which will help in collision prevention.
• It can also save fleet costs by neglecting breakdowns and potential collisions.

Conclusion

This blog covers some of the frequently asked Edge Computing interview questions that could be asked in data science interviews. Using these interview questions as a reference, you can better understand the concept of edge computing and start formulating effective answers for upcoming interviews. The major key takeaways from the discussed edge interview questions are-

1. Edge computing is a strategy to bring data processing and storage closer to the local system instead of the cloud; it will increase performance and decrease latency.

2. We discussed the meaning of edge, which implies where data is collected.

3. We have discussed its role in IoT. When the IoT object has enough storage and computing power to make low latency decisions and process data in milliseconds, we can take an edge device as a part of the IoT.

4. We have also compared edge computing with cloud computing.

5. At last, we discussed the two real-world applications, including fleet management and autonomous vehicles.

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Shikha

I am a tech enthusiast, a student, and a learner. I am a critical reader and a lover of words who finds writing blogs interesting. I possess the capability to research and learn new technologies quickly.