The implementation part felt manageable but they pushed hard on why I chose the data structures I did, specifically around memory.
Start by clarifying requirements and choosing a concrete system (text editor or bookkeeping) to design. Then walk through the core data structures and OOP design, explicitly comparing trade-offs in memory and time complexity, and justify your choices. Finally, outline implementation details and potential optimizations.
Pro tip: Demonstrate awareness of real-world constraints by discussing how your design would scale and handle edge cases, and be prepared to write pseudocode for critical operations. Show that you can balance theoretical complexity with practical engineering concerns.
Ask clarifying questions to understand the expected features, scale, and constraints (e.g., concurrent access, persistence, undo/redo). Define the core operations and non-functional requirements.
Sketch the main components and their interactions using OOP principles (e.g., classes, interfaces, inheritance). Choose a system (text editor or bookkeeping) and outline its architecture.
For each core operation, propose data structures (e.g., gap buffer, piece table, balanced BST, hash map) and compare their time/space complexities and memory usage. Justify your choices based on requirements.
Explain how key operations (insert, delete, search, undo) are implemented, including pseudocode if needed. Provide precise time and space complexity analysis for each operation.
Address scalability, concurrency, persistence, and potential optimizations. Mention how you would test and validate the design.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.