Start by clarifying requirements (e.g., offline support, real-time updates, security) and then present a layered architecture: a networking core (HTTP client, interceptors), a request orchestration layer (retry, caching, auth), and a data layer (repositories, models). Walk through alternatives like REST vs GraphQL, native vs cross-platform networking, and tradeoffs around performance, complexity, and maintainability.
Pro tip: Emphasize observability and error handling from the start—mention how you'd instrument requests, log failures, and handle edge cases like token refresh and network flakiness, as these are often overlooked but critical in production mobile apps.
Ask about expected traffic, offline capabilities, security needs, and platform targets to tailor the design. This shows you avoid over-engineering and focus on actual needs.
Propose a layered approach: a low-level HTTP client (e.g., URLSession, OkHttp, Retrofit), a middleware layer for cross-cutting concerns (auth, logging, retries), and a repository layer that abstracts data sources.
Discuss REST vs GraphQL, gRPC, or WebSockets, and justify based on use cases. Mention patterns like request queuing, caching strategies (memory/disk), and dependency injection for testability.
Compare tradeoffs: e.g., GraphQL reduces over-fetching but adds complexity; native networking is performant but platform-specific; third-party libraries speed development but add dependencies.
Include error handling, retry policies with exponential backoff, token refresh, network reachability, and analytics/logging. Mention testing strategies (unit, integration, mocking).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining the ViewModel's core purpose—managing UI-related data lifecycle-consciously—then contrast how it's scoped and cleared in plain Java (manual, often Activity/Fragment-bound), Kotlin (lifecycle-aware via ViewModelStore), and Compose (remember + viewModel() with LocalViewModelStoreOwner). Highlight pitfalls like leaks from holding Context, incorrect scoping, and recomposition issues, then suggest optimizations such as using SavedStateHandle, avoiding heavy work in init, and leveraging Compose's lifecycle-aware collection.
Pro tip: Emphasize that ViewModel is not a silver bullet for all state; in Compose, prefer rememberSaveable for UI state that doesn't need business logic, and use ViewModel only for screen-level state that survives configuration changes. Also mention that in plain Java, without AndroidX, you must manually handle onCleared and avoid static references.
Explain that ViewModel is designed to hold and manage UI-related data in a lifecycle-conscious way, surviving configuration changes and being cleared when its associated lifecycle owner is finished.
Describe how in plain Java (pre-AndroidX or manual), ViewModel lifecycle is often tied to Activity/Fragment with manual clearing; in Kotlin with AndroidX, it's scoped to ViewModelStoreOwner and cleared via onCleared; in Compose, it's obtained via viewModel() and scoped to the nearest ViewModelStoreOwner, with lifecycle-aware state collection.
Discuss common pitfalls: leaking Context/View references, incorrect scoping (e.g., using Activity context in ViewModel), performing long-running operations in init, and in Compose, recomposition triggering unnecessary ViewModel creation or state updates.
Suggest optimizations: use SavedStateHandle for process death, avoid heavy work in init, use coroutines with viewModelScope, in Compose use collectAsStateWithLifecycle, and consider separating UI state from business logic.
Conclude with trade-offs: ViewModel adds complexity but improves testability and lifecycle safety; in Compose, balance between ViewModel and rememberSaveable; always scope correctly and clean up resources.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Covered this as part of the broader system design discussion.
Start by defining concurrency in mobile as managing multiple tasks without blocking the UI, then outline a layered strategy: use structured concurrency with coroutines/async-await for high-level orchestration, and dispatch queues/threads for low-level control. Emphasize choosing patterns based on trade-offs like complexity, performance, and safety, and give examples of when to use each.
Pro tip: Show awareness of platform-specific pitfalls: on iOS, avoid data races with actors and @MainActor; on Android, use coroutine scopes tied to lifecycle to prevent leaks. Mention that you measure before optimizing—concurrency bugs are hard to debug, so prefer simplicity and testability.
Ask about the app's concurrency needs: UI responsiveness, background tasks, data consistency, and platform targets. This shows you tailor solutions to context.
Prefer structured concurrency (Kotlin Coroutines, Swift async/await) for readability and automatic cancellation. Explain how they simplify error handling and lifecycle management.
Use actors, serial queues, or locks to protect shared data. Discuss trade-offs: actors are safer but can bottleneck; locks are fast but error-prone.
Leverage main thread for UI, background queues for heavy work. Mention GCD/OperationQueue on iOS, Dispatchers on Android, and how to avoid priority inversion.
Use unit tests with fake schedulers, stress tests, and tools like Thread Sanitizer. Emphasize that concurrency bugs are subtle, so observability is key.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the app's requirements (e.g., complexity, deep linking, platform) to frame your answer. Then compare common navigation architectures (e.g., stack, tab, drawer, coordinator) and discuss trade-offs in terms of scalability, testability, and user experience. Conclude with a recommendation based on the context.
Pro tip: Emphasize that navigation architecture should be driven by the app's domain and team structure, not just technical trends. Mention how you'd measure success (e.g., reduced coupling, easier deep linking) to show a product-minded approach.
Ask about app complexity, number of screens, deep linking needs, and platform (iOS/Android). This ensures your answer is tailored to the actual problem.
Briefly describe stack-based, tab-based, drawer, and coordinator patterns. Mention that most apps use a combination (e.g., tabs with nested stacks).
Compare approaches on scalability, testability, deep linking, state restoration, and team workflow. For example, coordinators improve separation of concerns but add boilerplate.
Based on the requirements, suggest a specific architecture (e.g., coordinator pattern with a router) and justify why it fits. Mention how you'd handle edge cases like authentication flows.
Explain how you'd implement it (e.g., using a navigation graph or coordinator objects) and how it can evolve as the app grows. Highlight testing and maintenance benefits.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.