Google·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

Ran into a Google SWE coding problem that boils down to the classic difference array pattern. Not much context given about the round itself but the problem structure is pretty recognizable if you've seen range update problems before.

Questions Asked (1)

Given an array and a series of range update operations where each operation adds a value to every element between two indices, return the final array after all operations are applied.

Algorithms & Data Structures

Author's notes

The brute force is obvious but dies on large inputs.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and discuss the brute-force approach first, then introduce the difference array technique to achieve O(n + m) time. Explain how to apply range updates in O(1) each and then compute the final array via prefix sums.

Pro tip: Mention that the difference array technique is optimal for offline range updates and can be extended to 2D or multiple updates, showing depth. Also, discuss edge cases like overlapping updates and large values to demonstrate thoroughness.

1. Understand the problem

Restate the problem in your own words and ask clarifying questions about input size, update frequency, and whether updates are online or offline.

2. Discuss brute-force

Acknowledge that a naive approach would iterate over each range for each update, resulting in O(n*m) time, which may be inefficient for large inputs.

3. Introduce difference array

Explain that a difference array allows O(1) range updates by adding the value at the start index and subtracting it after the end index.

4. Apply updates and compute prefix sums

Iterate through all updates to modify the difference array, then compute the prefix sum to obtain the final array.

5. Analyze complexity and edge cases

State that time complexity is O(n + m) and space is O(n). Discuss handling of large values, negative updates, and out-of-bounds indices.

Key Points to Mention

Difference array technique for O(1) range updates
Prefix sum to reconstruct the final array
Time complexity: O(n + m) where n is array length and m is number of updates
Space complexity: O(n) for the difference array
Handling of edge cases: overlapping updates, large values, negative values
Comparison with alternative approaches like segment trees for online queries

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