This question is basically five questions duct-taped together.
Start by clarifying requirements and defining input/output schemas, then walk through a concrete example to illustrate the transformation pipeline. Structure your answer around a modular pipeline: parse and validate, expand recurrences, convert timezones, localize, resolve conflicts, and render. Finally, analyze time and space complexity and discuss trade-offs.
Pro tip: Emphasize correctness and edge cases (e.g., DST transitions, locale-specific formatting) and propose a testable, extensible design. Mention that you would validate assumptions with the interviewer before diving into implementation.
Ask clarifying questions about the schedule template format, user profile fields, and expected output. Define input and output schemas (e.g., JSON) with explicit types and constraints.
Outline a modular pipeline: parse and validate inputs, expand recurring events, convert timezones, localize date/time and text, resolve conflicts, and render the notification sheet.
Pick a sample event and user profile, and step through each stage of the pipeline, showing intermediate data and the final output. Highlight how edge cases (e.g., DST, overlapping events) are handled.
Derive time and space complexity for each stage and the overall algorithm. Discuss trade-offs between precomputation, streaming, and batch processing, and how they affect scalability.
Recap the solution, mention potential improvements (e.g., caching, parallelization), and ask if the interviewer wants to dive deeper into any area.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.