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Adobe · Redux
Practice Redux questions specifically asked in Adobe interviews – ideal for online test preparation, technical rounds and final HR discussions.
Questions
11
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Company
Adobe
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Subject
Redux
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Go through each question and its explanation. Use this page for targeted revision just before your Adobe Redux round.
A pure function always returns the same output for the same input and has no side effects like API calls, random number generation, or date/time dependencies. Redux reducers must be pure functions because this predictability is essential for debugging, testing, and features like time-travel. Any side effects should be handled in middleware like Redux Thunk or Redux Saga.
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Redux follows three fundamental principles: Single source of truth (one store), State is read-only (changes only through actions), and Changes are made with pure functions (reducers). These principles ensure predictability and make debugging easier by maintaining a clear data flow pattern.
Reducer composition is the pattern of breaking down a large reducer into smaller, focused reducers that each manage their own slice of state independently, making the code more maintainable and testable. The combineReducers function from Redux takes an object where keys are state slice names and values are reducer functions, and creates a root reducer that calls each slice reducer with its portion of state and returns a combined state object. This allows you to organize your reducers by feature or domain while Redux handles calling them appropriately and assembling the final state.
Mutation hooks like useCreatePostMutation return a tuple with a trigger function as the first element and a result object as the second, unlike query hooks that automatically fetch on mount. You call the trigger function manually when needed like onClick handlers passing the mutation argument, and the trigger returns a promise allowing you to await the result or use then and catch. The result object provides the same isLoading, isSuccess, isError, data, and error properties as query hooks, plus a reset function to clear the mutation state, but mutations don't cache results by default and must be explicitly triggered rather than running automatically.
Normalized state structure stores entities as objects keyed by ID like users: { byId: { 1: userData }, allIds: [1, 2] } which eliminates duplication, makes updates simpler, and improves performance by avoiding nested updates. This pattern treats your Redux store like a database with lookup tables, where relationships between entities are represented by IDs rather than nesting objects. Normalizr library or RTK's createEntityAdapter can help automate this normalization process.
You compose selectors by creating simple input selectors that extract raw state values, then combining them using createSelector from reselect which takes input selectors as arguments and a result function that transforms their outputs into derived data. Selector composition allows you to build complex selectors from simple reusable pieces, automatically memoizes results so expensive computations only run when inputs change, and maintains separation of concerns by keeping domain logic in selectors rather than components. You can create selector hierarchies where high-level selectors depend on lower-level ones, making your code more maintainable and testable.
Redux provides strict unidirectional data flow patterns that make large applications more predictable, exceptional debugging tools like time-travel debugging, and a massive ecosystem of middleware, tools, and community support. While alternatives like Zustand or Jotai are simpler for small apps with less boilerplate, Redux's structure and tooling become valuable in complex applications with multiple developers where consistency, debuggability, and established patterns prevent mistakes and make onboarding easier.
Essential best practices include using Redux Toolkit as the standard approach to reduce boilerplate and follow conventions, keeping state normalized and minimal with derived data in selectors, organizing code by feature rather than type for better maintainability, and using TypeScript for type safety across actions, state, and selectors. Always use memoized selectors for derived data to optimize performance, leverage RTK Query for data fetching to handle caching automatically, write comprehensive tests for reducers and selectors as pure functions, and use Redux DevTools effectively for debugging. Follow the principle of putting only truly global state in Redux while keeping local state in components, and ensure all state is serializable for proper debugging and persistence.
RTK Query keeps unused cached data for 60 seconds by default before removing it, which can be configured using the keepUnusedDataFor option at the API level or per endpoint. This automatic cache cleanup prevents memory leaks while ensuring frequently accessed data remains available, and the timer resets if the data gets used again within the window. You can adjust this duration based on your application's needs, using longer times for more stable data or shorter for rapidly changing data.
createAction is a helper function that generates an action creator for the given action type string, returning a function that takes a payload and returns a formatted action object. While createSlice handles this automatically for slice reducers, createAction is useful when you need standalone action creators or want to share actions across multiple slices. It ensures consistent action object structure and reduces boilerplate.
createAsyncThunk is an RTK function that generates thunks for async operations, automatically dispatching pending, fulfilled, and rejected actions based on the promise returned by your async function. It simplifies async logic by eliminating the need to manually create three action types and three action creators for each async operation, handles promise lifecycle automatically, and provides consistent patterns for loading states and error handling. You define the async function and then handle the generated actions in extraReducers to update state accordingly, making async code more maintainable and reducing boilerplate significantly.