Google·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Apr 2026

Summary

Google SWE interview with a tricky array problem that seems straightforward until you hit the follow-up. The O(1) space constraint is where things get interesting.

Questions Asked (1)

Given an integer array, find a pair of indices (i, j) where i <= j and the values at both indices are equal, such that the subarray sum between them is maximized. Return those two indices.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

First pass I went straight for a hashmap to track first occurrences of each value, then scan and compute subarray sums.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem: we need to find two indices with equal values that maximize the sum of the subarray between them. Then, use a hash map to store the first occurrence of each value and compute the maximum sum for each pair, tracking the best indices. Alternatively, precompute prefix sums to quickly calculate subarray sums.

Pro tip: Mention that if all numbers are non-negative, the maximum sum for a value is achieved by the first and last occurrence, but if negatives are allowed, you must consider all pairs. This shows you understand edge cases and can adapt your approach.

1. Clarify the problem

Ask if the array can contain negative numbers and if the subarray sum includes the endpoints. Confirm that i <= j and that we need to return the indices, not the sum.

2. Consider brute force

A naive O(n^2) approach checks all pairs of equal values and computes the sum. This is simple but inefficient for large arrays.

3. Optimize with hash map and prefix sums

Use a hash map to store the first occurrence of each value. For each subsequent occurrence, compute the subarray sum using prefix sums and update the maximum. This reduces time to O(n) with O(n) extra space.

4. Handle edge cases

Consider arrays with all distinct elements (no pair), negative numbers, and large input sizes. Discuss how the algorithm handles these cases.

5. Analyze trade-offs

Compare the brute force and optimized approaches in terms of time and space complexity. Mention that the hash map approach is optimal for general cases.

Key Points to Mention

Time and space complexity of the solution (O(n) time, O(n) space).
Use of prefix sums to compute subarray sums in O(1) time.
Handling of negative numbers and why first/last occurrence may not always be optimal.
Edge cases: empty array, single element, all distinct elements.
Trade-offs between brute force and optimized approach.
Returning the indices, not the sum, and ensuring i <= j.

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