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Clarify the problem constraints and edge cases, then identify the heap-based pattern (e.g., top K, merging sorted lists, scheduling). Explain the heap operations and complexity, then implement cleanly with tests.
Pro tip: Verbalize your thought process and trade-offs; interviewers value clear reasoning over silent coding. Also, consider if a heap is truly optimal—sometimes a quickselect or bucket sort is better.
Restate the problem in your own words, ask about input size, data types, and edge cases (empty input, duplicates, etc.).
Determine if the problem fits a heap pattern (e.g., kth largest, merge k sorted, scheduling). Discuss alternative approaches and why heap is suitable.
Outline the algorithm step-by-step, specify heap type (min/max), and analyze time/space complexity. Mention any optimizations.
Write clean code with meaningful variable names. Walk through a small example and test edge cases.
Check for off-by-one errors, discuss potential improvements, and confirm complexity with the interviewer.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.