Heap was the obvious move here and I went straight for it.
Clarify requirements and edge cases, then choose a min-heap (priority queue) keyed by deadline for O(log n) insertion and O(log n) extraction. Implement the two functions with clean interfaces, and analyze time/space complexity.
Pro tip: Mention that if deadlines are small integers, a bucket queue can give O(1) operations, showing you consider trade-offs beyond the textbook heap solution.
Ask about input size, whether deadlines are unique, expected operation frequency, and if the queue must be thread-safe.
Select a min-heap (priority queue) for O(log n) insert and extract-min, or a bucket queue for O(1) if deadlines are bounded integers.
Define function signatures: addTasks(tasks) and processNextTask() returning the task with smallest deadline, handling empty queue.
Code the heap operations, write unit tests for edge cases (empty input, duplicate deadlines, single task), and verify correctness.
State time and space complexity for each operation and discuss potential optimizations or alternative structures.
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