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Media.net·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Media.net SWE interview with a competitive programming style problem that had a twist I wasn't ready for. The core question was manageable but the M-queries extension changed the whole complexity story.

Questions Asked (1)

Q1

Given an array of N numbers and a target T, find a contiguous subarray whose sum is closest to T. Then handle M different values of T efficiently.

Algorithms & Data Structures
Author's notes

My first instinct was prefix sums plus binary search, which gets you to O(N log N) per query.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem constraints and define 'closest' (absolute difference). For a single query, use prefix sums and a balanced BST (or sorted list) to find the subarray sum closest to T in O(N log N). For multiple queries, precompute all possible subarray sums (O(N^2)) and sort them; then for each query, use binary search to find the closest sum in O(log N) per query.

Pro tip: Mention the trade-off between preprocessing time and query time: if M is large, O(N^2) preprocessing is acceptable; if M is small, per-query O(N log N) might be better. Also, discuss handling negative numbers and the importance of using a TreeSet or balanced BST for efficient closest-sum queries.

1. Clarify the problem

Ask about constraints: N, M, range of numbers (negative?), and definition of 'closest' (absolute difference). Confirm if subarray must be non-empty.

2. Single query approach

Use prefix sums and a balanced BST (e.g., TreeSet in Java) to find the subarray sum closest to T in O(N log N). Iterate through prefix sums, for each prefix sum s, find the closest value to T - s in the BST.

3. Multiple queries approach

Precompute all subarray sums in O(N^2) and store them in a sorted array. For each query T, use binary search to find the closest sum in O(log N) per query.

4. Optimize and compare

Compare the two approaches based on M. If M is large, precomputation is better; if M is small, per-query might be sufficient. Discuss space-time trade-offs.

5. Handle edge cases

Consider empty subarray? Negative numbers? Large N causing O(N^2) memory issues? Discuss alternative data structures like segment trees if needed.

Key Points to Mention

  • Prefix sums to compute subarray sums efficiently
  • Balanced BST (TreeSet) for closest sum queries in O(log N)
  • Precomputation of all subarray sums for multiple queries
  • Binary search on sorted subarray sums
  • Time complexity: O(N log N) per query vs O(N^2 + M log N) for precomputation
  • Handling negative numbers and absolute difference

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