Microsoft·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Microsoft coding interview, one question about arrays and sums. Pretty short session, nothing too crazy but the problem has some tricky edge cases if you're not warmed up.

Questions Asked (1)

Given an array of integers and a target sum, find the longest contiguous sub-array whose elements add up to that sum.

Algorithms & Data Structures

Author's notes

Went with a sliding window first, then realized that only works cleanly for positive numbers.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., positive/negative numbers, empty subarray allowed) and then propose an optimal solution using a hash map to store prefix sums and their earliest indices, enabling O(n) time. Walk through the algorithm step-by-step, handle edge cases, and analyze time/space complexity.

Pro tip: Mention that if all numbers are positive, a sliding window approach works, but for general integers, the prefix sum with hash map is necessary. This shows you understand the problem's nuances and can adapt your solution based on constraints.

1. Clarify requirements and constraints

Ask about the range of integers (positive, negative, zero), whether the subarray must be non-empty, and if multiple valid subarrays exist, which one to return (e.g., longest, any).

2. Discuss brute force and optimal approaches

Mention that a brute force O(n^2) solution checks all subarrays, but an optimal O(n) solution uses prefix sums and a hash map to track the earliest index of each prefix sum.

3. Explain the prefix sum algorithm

Iterate through the array, maintaining a running sum. For each sum, check if (sum - target) exists in the hash map; if so, update the longest length. Store the current sum with its index only if not already present.

4. Handle edge cases and analyze complexity

Consider cases like empty array, no valid subarray, target zero, and negative numbers. State that time complexity is O(n) and space complexity is O(n) due to the hash map.

5. Test with examples and conclude

Walk through a small example to verify correctness, then summarize the solution and its advantages over brute force.

Key Points to Mention

Prefix sum technique: cumulative sum from start to current index.
Hash map storing prefix sum to earliest index for O(1) lookups.
Handling negative numbers and zeros, which break sliding window approaches.
Time complexity O(n) and space complexity O(n).
Edge cases: empty array, no solution, target sum zero, multiple solutions.
Comparison with sliding window for positive-only arrays.

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