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Meta·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Apr 2026

Summary

Meta coding screen, probably mid-level, sliding window type problem with a twist. Not the hardest thing I've seen from them but the edge cases will get you if you're not careful.

Questions Asked (1)

Q1

Given an integer array, find the length of the longest contiguous subarray that is strictly increasing, except you're allowed one position where the value drops. One break in the strictly-increasing rule is okay, but only one.

Algorithms & Data Structures
Author's notes

My first instinct was two pointers and I started coding before really thinking through what happens when the drop occurs mid-window.

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AI HintsAI Generated

Suggested Approach

Use a sliding window with two pointers to maintain a window that is strictly increasing except for at most one drop. Expand the right pointer, and when a second drop is encountered, move the left pointer to just after the first drop to restore validity. Track the maximum window length throughout.

Pro tip: Clarify edge cases upfront, such as empty arrays or arrays with all equal elements, and mention that the solution runs in O(n) time and O(1) space, which is optimal for this problem.

1. Understand the problem and constraints

Restate the problem: find the longest contiguous subarray that is strictly increasing except for at most one drop. Ask clarifying questions about edge cases and input size.

2. Choose the right technique

Select a sliding window approach because it efficiently tracks a valid window and adjusts when the condition is violated. Mention that a brute-force check would be O(n^2) and is not optimal.

3. Define window validity and update rules

Maintain a window [left, right] that is valid. When adding arr[right], if arr[right] <= arr[right-1], increment a drop counter. If drop counter exceeds 1, move left to the position after the previous drop (i.e., left = last_drop_index + 1) and reset the drop counter appropriately.

4. Implement and track maximum length

Iterate right from 0 to n-1, update the window, and after each step update max_len = max(max_len, right - left + 1). Handle the case when a drop occurs by updating last_drop_index.

5. Analyze complexity and test

State that the algorithm runs in O(n) time and O(1) space. Walk through a few test cases, including arrays with no drops, one drop, multiple drops, and edge cases like empty or single-element arrays.

Key Points to Mention

  • Sliding window technique with two pointers (left and right) to maintain a valid subarray.
  • Tracking the number of drops (violations of strict increase) and the index of the last drop.
  • When a second drop occurs, moving left to last_drop_index + 1 to restore validity.
  • Time complexity O(n) and space complexity O(1).
  • Handling edge cases: empty array, single element, all equal elements, and arrays with multiple drops.
  • Comparing with brute-force O(n^2) approach to highlight efficiency.

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