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Meta·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Jun 2026

Summary

Meta coding round, sliding window problem. Pretty standard stuff if you've seen it before, brutal if you haven't.

Questions Asked (1)

Q1

Given an integer array and a window size k, return the maximum value in each contiguous subarray of size k as the window moves from left to right.

Algorithms & Data Structures
Author's notes

Knew the brute force immediately but sat there second-guessing whether to just code it up or explain the optimal path first.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., array size, k value, data types) and then propose an efficient solution using a monotonic deque to achieve O(n) time complexity. Explain the algorithm step-by-step, emphasizing how the deque maintains indices of potential maximums, and then discuss edge cases and complexity.

Pro tip: Mention that a naive O(n*k) solution is straightforward but suboptimal; by using a deque, you can achieve O(n) and handle large inputs gracefully. Also, proactively discuss how you would test the solution with edge cases like k=1, k=n, and arrays with negative numbers.

1. Clarify requirements and constraints

Ask about input size, possible values, and whether the array can be empty or k can be larger than the array. Confirm the expected output format.

2. Discuss brute-force and optimal approaches

Acknowledge the O(n*k) brute-force method, then introduce the monotonic deque approach that processes each element once, achieving O(n) time.

3. Explain the deque algorithm in detail

Describe how to maintain a deque of indices where values are in decreasing order. For each element, remove indices out of the window and smaller elements from the back, then add the current index. The front of the deque is the maximum for the current window.

4. Walk through an example

Choose a small array (e.g., [1,3,-1,-3,5,3,6,7] with k=3) and trace the deque operations to demonstrate correctness and build intuition.

5. Analyze complexity and edge cases

State that time complexity is O(n) and space is O(k). Discuss handling k=1, k=n, empty array, and negative numbers, and mention potential pitfalls like integer overflow.

Key Points to Mention

  • Monotonic deque data structure and its role in maintaining window maximums
  • Time complexity: O(n) vs. O(n*k) brute force
  • Space complexity: O(k) for the deque
  • Handling edge cases: k=1, k=n, empty array, negative numbers
  • Step-by-step algorithm: removing out-of-window indices, maintaining decreasing order, and outputting front element
  • Testing strategy: unit tests with various inputs and edge cases

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