This one was rough before I even typed a line.
Start by clarifying the problem requirements and constraints, then identify the core entities and their relationships. Design the class hierarchy using appropriate design patterns, and implement a clean, extensible solution while discussing trade-offs.
Pro tip: At Citadel, interviewers value code quality and performance. Before coding, briefly outline your design and ask if the interviewer prefers a particular pattern or has any constraints, showing collaboration and foresight.
Ask questions to understand functional and non-functional requirements, such as expected scale, performance needs, and any specific features. This ensures you design the right system.
Determine the main objects, their attributes, and behaviors. Define relationships like inheritance, composition, and associations.
Apply SOLID principles and design patterns (e.g., Factory, Strategy, Observer) to create a flexible and maintainable class diagram. Consider interfaces and abstract classes.
Write clean, modular code with meaningful names and comments. Focus on correctness and extensibility, and handle edge cases.
Explain why you chose certain patterns, discuss alternative approaches, and suggest how the design could scale or adapt to new requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Got the solution coded up fully, which felt good.
Start by clarifying the problem and constraints, then walk through a brute-force solution before optimizing. Implement the optimal solution with clean code, test with edge cases, and discuss trade-offs, optimizations, and alternatives.
Pro tip: At Citadel, interviewers value not just correct solutions but also the ability to articulate trade-offs and think about scalability. Always quantify time/space complexity and relate it to real-world performance.
Ask clarifying questions to confirm input/output formats, constraints, and edge cases. Restate the problem in your own words to ensure alignment.
Discuss a brute-force solution first, then propose optimizations. Compare time and space complexity of each approach.
Write clean, modular code for the chosen approach. Walk through test cases, including edge cases, to verify correctness.
Explain the trade-offs between different solutions (e.g., time vs. space, readability vs. performance). Discuss potential optimizations and alternative algorithms.
Consider how the solution would scale with larger inputs or distributed systems. Mention any relevant real-world applications or further optimizations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.