Pretty standard heap question once you see what they're asking.
Clarify the scheduling objective (e.g., maximize number of tasks completed, minimize lateness) and constraints (single machine, preemption). Then model it as a classic scheduling problem: sort tasks by deadline and use a greedy algorithm with a min-heap to select tasks, or use dynamic programming for weighted variants. Discuss time/space complexity and potential optimizations.
Pro tip: Always state your assumptions and ask clarifying questions before diving into code; interviewers value candidates who identify ambiguities and edge cases (e.g., tasks with equal deadlines, zero or negative deadlines) and handle them gracefully.
Ask about the scheduling goal (e.g., maximize number of tasks, minimize maximum lateness), whether tasks can be preempted, if multiple machines are allowed, and any constraints on task durations.
For maximizing number of tasks on a single machine, use a greedy approach: sort tasks by deadline and use a min-heap to keep track of selected tasks, replacing the longest task if a deadline is missed. For weighted tasks, consider dynamic programming.
Trace the algorithm on a small example to verify correctness and demonstrate understanding of edge cases, such as tasks with the same deadline or tasks that cannot be scheduled.
State the time complexity (e.g., O(n log n) due to sorting and heap operations) and space complexity (O(n)). Discuss potential improvements or alternative approaches if needed.
Write clean, modular code with clear variable names, and test with edge cases (empty list, single task, all tasks missed). Be prepared to explain your code and handle follow-up questions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.