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Amazon·Software Engineer·Online Assessment (OA)·Intermediate

Intermediate
Jun 2026

Summary

Got an Amazon SWE online assessment with a server memory optimization problem. Pretty clean algorithmic question once you see the pattern, but the explanation in the problem statement is a bit misleading if you read it too fast.

Questions Asked (1)

Q1

Given an array of server memory capacities (always even length), split the servers into two equal groups where every primary server has at least one backup server with equal or greater memory. Maximize the total memory of the primary group.

Algorithms & Data Structures
Author's notes

The example in the problem says 3+3=6 which is clearly a typo (should be 3+4=7...

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AI HintsAI Generated

Suggested Approach

Sort the array, then pair the smallest half with the largest half to maximize the sum of the larger elements. The primary group consists of the larger elements from each pair, ensuring each has a backup with equal or greater memory.

Pro tip: After sorting, the optimal primary group is simply the second half of the sorted array. This greedy pairing is optimal because any other pairing would force a larger element to be a backup for a smaller one, reducing the total primary memory.

1. Understand the problem

Clarify that we need to partition the array into two equal-sized groups (primary and backup) such that each primary has a backup with memory >= primary's memory, and the sum of primary memories is maximized.

2. Sort the array

Sort the memory capacities in non-decreasing order. This allows us to easily pair smaller elements with larger ones.

3. Pair smallest with largest

Pair the first half (smallest n/2 elements) with the second half (largest n/2 elements) in order: smallest with smallest of the larger half, etc. This ensures each primary (from the larger half) has a backup (from the smaller half) with <= memory.

4. Compute the sum

The primary group is the second half of the sorted array. Sum these elements to get the maximum total primary memory.

5. Verify with examples

Test with small examples to confirm the greedy approach works and consider edge cases like duplicate values.

Key Points to Mention

  • Sorting is the key to simplifying the pairing problem.
  • Greedy pairing: smallest with largest ensures feasibility and maximizes sum.
  • Proof of optimality: any other pairing would require a larger element to be a backup for a smaller one, reducing the primary sum.
  • Time complexity: O(n log n) due to sorting, which is optimal for this problem.
  • Space complexity: O(1) extra space if sorting in place, or O(n) if using additional arrays.
  • Edge cases: all equal elements, negative values (though memory capacities are non-negative), and large input sizes.

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