My first instinct was a linear scan and I almost said it out loud before catching myself.
Clarify that the ranges are sorted and non-overlapping, then propose storing the start points in a sorted array and using binary search to find the range containing x. Discuss the trade-offs of this approach versus alternatives like interval trees or hash maps, and analyze time and space complexity.
Pro tip: Mention that if the ranges are static, binary search on sorted starts is optimal; if dynamic, consider a balanced BST or interval tree. Also, handle edge cases like x exactly on a boundary and empty input.
Ask if the ranges are sorted, non-overlapping, and static. Confirm that queries are frequent and that we need efficient point queries.
Propose storing the start points in a sorted array and using binary search to find the largest start <= x, then check if x <= end of that range.
State that preprocessing takes O(n log n) if sorting is needed, but if already sorted, O(n). Each query is O(log n) time, and space is O(n).
Mention interval trees or balanced BSTs for dynamic updates, or hash maps for O(1) if ranges are small and dense, but note their limitations.
Cover empty input, x before first range, x after last range, and x exactly on a boundary (inclusive/exclusive).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.