Meta·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026

Summary

Meta SWE coding round, one problem but they really dug into it. Not just 'does your code work' but 'explain every tradeoff you made and why.' Felt more like a design conversation than a pure leetcode session.

Questions Asked (1)

Given an integer array and an integer k, count the number of contiguous subarrays whose elements sum to k. You need a solution better than O(n²), and you have to explain your time and space complexity, how you handle negative numbers and zeros, and any integer overflow concerns.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The subarray sum part I knew, prefix sums with a hashmap, seen it before.

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AI HintsAI Generated

Suggested Approach

Use a hash map to store prefix sums and their frequencies, allowing you to find subarrays summing to k in O(n) time. For each element, compute the running sum and check if (running sum - k) exists in the map, adding its frequency to the count. Then, update the map with the current running sum.

Pro tip: Explicitly discuss how negative numbers and zeros affect the prefix sum approach, and mention that using a hash map with prefix sums handles them naturally. Also, bring up integer overflow and suggest using 64-bit integers for the prefix sum to avoid overflow.

1. Clarify the problem and constraints

Confirm that the array can contain negative numbers and zeros, and discuss the expected input size to determine if O(n) is necessary. Ask about integer overflow concerns if the sum can exceed 32-bit range.

2. Explain the prefix sum + hash map approach

Describe how to maintain a running sum and a hash map that counts occurrences of each prefix sum. For each element, check if (current sum - k) exists in the map to count valid subarrays ending at the current index.

3. Walk through an example

Choose a small array with negatives and zeros, and step through the algorithm to demonstrate correctness, showing how the map is updated and how counts are accumulated.

4. Analyze time and space complexity

State that the algorithm runs in O(n) time and uses O(n) space in the worst case due to the hash map. Contrast with the O(n²) brute-force approach.

5. Address edge cases and overflow

Discuss handling of negative numbers, zeros, and large sums. Mention using a 64-bit integer for the running sum to prevent overflow, and note that the hash map keys should also be 64-bit if necessary.

Key Points to Mention

Prefix sum technique: running sum and hash map to store frequencies.
Time complexity: O(n) average case, O(n²) worst case if hash collisions are not handled well, but typically O(n).
Space complexity: O(n) for the hash map.
Negative numbers and zeros are handled naturally because prefix sums can repeat or decrease.
Integer overflow: use 64-bit integers (e.g., long in Java, int64 in Python) for the running sum and map keys.
Initialization: map should start with {0: 1} to account for subarrays starting at index 0.

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