Summary
I interviewed for a React Native role at Coursefinder.ai across two rounds. The process involved a mix of coding, UI implementation, and in-depth discussions on React Native specific optimizations and features. Unfortunately, I was rejected after the second round.
Full Experience
First Round (BarRaiser)
The first round, conducted by a third-party (BarRaiser), started with a simple coding question: I was asked to print unique pairs like (1,2) and (3,4). Following that, a React Native challenge involved building a list with an input box that would filter the list items dynamically as I typed.
Second Round (Pure React Native Focus)
The second round was intensely focused on React Native. Key discussion points included optimizing FlatList performance for large datasets and strategies for efficient rendering of large images. I was also questioned on how to programmatically determine if the phone is in portrait or landscape mode, and the implementation details of deep linking in React Native.
Ultimately, I was informed that I was rejected after this round.
Interview Questions (6)
Given a set of pairs, print only the unique pairs. The example provided was (1,2), (3,4).
Build a React Native list component that includes an input box. The list items should be dynamically filtered in real-time as the user types into the input box.
Discuss strategies and techniques to optimize the performance of a React Native FlatList, especially when dealing with large datasets.
Explain methods and best practices for optimizing the rendering and performance of large images within a React Native application.
Describe how to programmatically determine whether a React Native application is running in portrait or landscape orientation on a mobile device.
Explain the concept of deep linking in React Native, how it works, and how to implement it in an application.