First, clarify the specific modification to the input data and its implications on the problem constraints. Then, analyze how the existing solution's assumptions break and propose adjustments, discussing trade-offs in time/space complexity and edge cases.
Pro tip: Demonstrate that you consider not just the algorithmic change but also the impact on code maintainability and testability, which is highly valued at Apple.
Ask questions to understand exactly how the input data is modified (e.g., size, range, distribution, duplicates, sortedness) and any new constraints.
Review the original solution and pinpoint which assumptions (e.g., sorted input, unique elements, bounded values) are invalidated by the modification.
Suggest changes to the algorithm or data structures to handle the new input, explaining why they work and their complexity.
Compare the adjusted solution with alternatives, highlighting trade-offs in time, space, and code complexity.
Mention how you would test the modified solution, including edge cases introduced by the new input characteristics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The hiring manager ran this round and it was way more coding-heavy than I expected from a system design slot.
Start by clarifying requirements and constraints, then sketch a high-level architecture with key components and trade-offs, implement a simplified version focusing on core logic, and finally write test cases covering normal, edge, and failure scenarios. Emphasize how your design and tests align with Apple's values of quality, performance, and user experience.
Pro tip: Verbalize your thought process and explicitly connect design decisions to testability; Apple values engineers who build quality in from the start and can articulate trade-offs clearly.
Ask questions to understand functional and non-functional requirements, scale, latency, and any Apple-specific constraints (e.g., privacy, ecosystem integration).
Sketch the main components, data flow, and interfaces; discuss trade-offs between different approaches (e.g., monolithic vs. microservices, SQL vs. NoSQL).
Write clean, modular code for the critical path, using appropriate data structures and algorithms; explain your choices as you go.
Outline unit, integration, and edge-case tests; include performance and failure scenarios to validate robustness.
Summarize how the design meets requirements, discuss potential improvements, and invite feedback to show collaboration.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.