In my interview with Solartis, I had a good experience as the discussion mainly focused on machine learning algorithms and core topics within the data science domain. I was asked to explain different ML models, their use cases, and how they work, along with related concepts such as data preprocessing, feature engineering, and model evaluation. The interview helped me demonstrate my understanding of data science fundamentals and also gave me valuable insight into the technical expectations for the role.

What is the purpose of React? Define a component in React. What are props in React? Explain the use of state in React. What is JSX in React? What are functional components? What is the virtual DOM? Explain the concept of lifting state up. What is the difference between props and state? Describe React lifecycle methods. What is useEffect used for? How does useState work? What is the purpose of useRef? What is the context API? Explain conditional rendering in React. What is a React fragment? Describe controlled vs uncontrolled components. What is a higher-order component? What are keys in React lists? What is reconciliation in React? How is React different from Angular? What is code splitting in React? What is lazy loading in React? Explain the useCallback hook. What does useMemo do? What is a pure component? How do you optimize performance in React? What is server-side rendering? What are React portals? How does error boundary work?