Meta·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Meta coding screen, looked like a pretty focused algorithms round. One question on binary search over a sorted array, nothing crazy, but they pushed on the reasoning behind approach choices.

Questions Asked (1)

Given a sorted array of integers and a query range [a, b], return all values in the array that fall within that range (inclusive). What's your approach and complexity?

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Went with bisect_left and bisect_right to find the bounds, then just sliced the array.

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AI HintsAI Generated

Suggested Approach

Use binary search to find the leftmost index where the value is >= a and the rightmost index where the value is <= b, then return the subarray between them. This leverages the sorted property to achieve O(log n + k) time, where k is the number of elements in the range.

Pro tip: Mention that you can use the same binary search helper for both bounds (lower_bound for a and upper_bound for b) to keep code clean and avoid off-by-one errors. Also note that if the array is huge and k is small, this is optimal, but if k is large, returning a copy is O(k) and you might discuss returning a view or iterator range instead.

1. Clarify assumptions and edge cases

Confirm the array is sorted ascending, handle empty array, a > b, and ranges outside the array bounds. Ask whether to return values or indices, and whether duplicates are allowed.

2. Choose binary search for bounds

Explain that linear scan is O(n) but binary search finds the start and end indices in O(log n). Use lower_bound for a and upper_bound for b (or equivalent).

3. Detail the binary search logic

Describe how to find the first index with value >= a and the last index with value <= b, being careful with inclusive bounds and duplicates. Mention using a single helper or two separate searches.

4. Extract and return the subarray

If the start index exceeds the end index, return an empty result. Otherwise, return the slice from start to end inclusive, noting that this takes O(k) time to copy.

5. State complexity and trade-offs

Time: O(log n + k) where k is the number of elements in range; space: O(k) for the output (or O(1) extra if returning indices). Discuss alternatives like linear scan for small arrays or when k is close to n.

Key Points to Mention

Binary search for lower and upper bounds (lower_bound/upper_bound) to achieve O(log n) search time.
Overall time complexity O(log n + k) and space complexity O(k) for the output.
Handling edge cases: empty array, a > b, no elements in range, duplicates.
Trade-off: linear scan is simpler and O(n) but binary search is better for large n and small k.
Possibility of returning indices or a view/iterator range instead of copying to avoid O(k) space.
Code clarity: using a single binary search helper for both bounds reduces bugs.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.