Google·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate

May 2026

Summary

Google SWE coding round with a sliding window problem that had a twist I wasn't fully prepared for. The core idea isn't hard but the exclusion mechanic tripped me up a bit on the implementation.

Questions Asked (1)

Given an integer array, a window size, and an integer k, slide a window across the array one step at a time. For each window, remove the k largest elements and return the average of what's left. Return all such averages as an array.

Algorithms & Data Structures

Author's notes

I got the sliding part pretty quickly but fumbled on efficiently dropping the k largest.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then propose an efficient algorithm using a sliding window with a data structure that supports fast removal of the k largest elements, such as a max-heap or a balanced BST. Analyze the time and space complexity, and discuss potential optimizations or trade-offs.

Pro tip: Mention that if k is small relative to the window size, a heap-based approach is efficient, but if k is large, consider using order-statistic trees or sorting-based methods. Also, handle edge cases like k >= window size by returning 0 or an empty array.

1. Clarify Requirements

Ask about constraints: array size, window size, k, possible negative numbers, and what to return if k >= window size. Confirm output format (e.g., floating-point averages).

2. Design Algorithm

Propose maintaining a data structure for each window that allows efficient removal of the k largest elements. For example, use a max-heap to extract k largest, then compute the sum of remaining elements.

3. Optimize for Sliding

Discuss how to update the data structure when the window slides: remove the outgoing element and add the incoming element. Consider using a balanced BST or two heaps to maintain order statistics.

4. Analyze Complexity

Calculate time complexity: O(n * (k log w + w)) if using a heap per window, or O(n log w) with a more advanced structure. Space complexity O(w).

5. Handle Edge Cases

Address cases where k >= window size (return 0), empty array, or window size > array length. Also discuss integer overflow and floating-point precision.

Key Points to Mention

Sliding window technique to avoid recomputing from scratch for each window.
Use of a max-heap or priority queue to efficiently find and remove the k largest elements.
Time complexity analysis: O(n * k log w) vs. O(n log w) with advanced data structures.
Space complexity: O(w) for storing the window elements.
Edge cases: k >= window size, empty array, negative numbers, and floating-point precision.
Trade-offs between different data structures (heap vs. balanced BST vs. sorting).

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