This was part 3 of a multi-part coding problem.
Clarify the data model and consumption semantics first, then outline a function that checks the task's consumed status before updating the deadline, returning null if consumed. Discuss edge cases like missing tasks, invalid deadlines, and concurrency, and propose tests to validate the behavior.
Pro tip: Mention that returning null for consumed tasks is a deliberate API contract choice, and suggest logging or metrics to track attempts to update consumed tasks, which helps with debugging and monitoring in production ML pipelines.
Ask about the task object structure, how 'consumed' is represented, and whether the function should mutate in place or return a new object. Confirm the expected return type and error handling.
Define the function parameters (e.g., task ID, new deadline) and outline the check: if task is consumed, return null; otherwise update the deadline and return the updated task.
Consider missing tasks, invalid deadline formats, and race conditions where a task might be consumed between check and update. Propose atomic operations or locking if needed.
Write clean code with clear comments, then outline unit tests covering normal update, consumed task, missing task, and concurrent scenarios.
Explain how this function fits into the larger system, and suggest logging or metrics for attempts to update consumed tasks to aid observability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Only came up right at the end, after all three parts were done.
Start by clearly stating the time complexity using Big-O notation, then briefly explain how you derived it by analyzing the loops, recursion, or operations in your code. Also mention space complexity if relevant, and discuss any trade-offs you made between time and space.
Pro tip: Always relate the complexity to the input size and mention best, average, and worst cases if they differ. If you optimized from a brute-force solution, explain the improvement to show your problem-solving process.
Clearly state the time complexity in Big-O notation, e.g., O(n log n). If there are multiple parts, specify the overall complexity.
Walk through the code or algorithm, identifying the dominant operations and how they scale with input size. For example, nested loops multiply, sequential loops add.
If relevant, state the space complexity and explain how extra data structures or recursion contribute to it.
If you made a trade-off (e.g., using extra space to reduce time), explain why it was beneficial and any alternatives considered.
Mention if the complexity changes for best, average, or worst-case scenarios, and how it handles edge cases like empty input.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.