Amazon·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Amazon SWE coding round with a frequency-based sort problem. Pretty standard algorithmic stuff but the tiebreak rule is where people trip up, and I almost did too.

Questions Asked (1)

Given an array of integer error codes with duplicates, sort them by ascending frequency. For equal frequencies, the smaller numeric value should come first. All elements must be preserved in the output.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I almost sorted on (freq, -value) out of muscle memory because I'd practiced a version where ties break by descending value.

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AI HintsAI Generated

Suggested Approach

First, count the frequency of each error code using a hash map. Then, sort the unique codes by frequency ascending and, for ties, by numeric value ascending. Finally, expand the sorted unique codes back into the output array by repeating each code according to its frequency.

Pro tip: Mention that you can achieve O(n log k) time (where k is the number of unique codes) by sorting only the unique elements, which is more efficient than sorting the entire array when there are many duplicates. Also, clarify that the output must preserve all elements, so the result length equals the input length.

1. Clarify requirements and edge cases

Confirm that the output should contain all original elements, sorted by frequency ascending, with ties broken by smaller numeric value first. Discuss edge cases like empty array, single element, or all elements identical.

2. Count frequencies

Use a hash map to count the occurrences of each unique error code. This takes O(n) time and O(k) space, where k is the number of unique codes.

3. Sort unique codes

Extract the unique codes and sort them using a custom comparator: primarily by frequency ascending, and secondarily by numeric value ascending. This takes O(k log k) time.

4. Reconstruct output array

Iterate through the sorted unique codes and append each code to the result array exactly as many times as its frequency. The result will have the same length as the input.

5. Analyze complexity and trade-offs

State the overall time complexity O(n + k log k) and space complexity O(k). Compare with alternative approaches like sorting the entire array first (O(n log n)) and explain why the frequency-counting method is more efficient when duplicates are many.

Key Points to Mention

Use a hash map to count frequencies efficiently.
Sort only the unique elements, not the entire array, to reduce time complexity.
Custom comparator: sort by frequency ascending, then by value ascending.
Reconstruct the output by repeating each unique element according to its frequency.
Time complexity: O(n + k log k), space complexity: O(k), where k is the number of unique elements.
Edge cases: empty array, single element, all elements same, negative error codes (if applicable).

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