Amazon·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Amazon Data Scientist technical screen, one main coding problem with a follow-up that caught me a bit flat-footed. Classic sliding window territory but the extension to collecting all qualifying substrings added some pressure.

Questions Asked (1)

Write a Python function that returns the length of the longest substring without any repeating characters. Then, modify it to return all substrings that actually achieve that maximum length.

Algorithms & Data Structures

Author's notes

Got the first part pretty cleanly, sliding window with a dict tracking last-seen indices, nothing too painful.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and edge cases, then implement the sliding window algorithm to find the maximum length in O(n) time. For the extension, modify the algorithm to collect all substrings that achieve the maximum length, ensuring no duplicates and considering all valid windows.

Pro tip: Discuss the trade-offs between time and space complexity, and mention that while the sliding window approach is optimal for the length, returning all substrings may require additional space proportional to the number of such substrings. Also, consider if the interviewer expects substrings to be unique or if overlapping occurrences are allowed.

1. Clarify requirements and edge cases

Ask clarifying questions: Are we dealing with ASCII or Unicode? Should the function return unique substrings or all occurrences? What about empty strings? This shows attention to detail.

2. Explain the sliding window approach

Describe how to use two pointers (left and right) and a set to track characters in the current window, expanding right and shrinking left when a duplicate is found, updating the maximum length.

3. Implement the function for maximum length

Write clean Python code for the first part, ensuring O(n) time complexity. Use a dictionary to store the last index of each character for optimization.

4. Modify to collect all maximum-length substrings

Adapt the algorithm to record substrings when the current window length equals the maximum. Use a list to store results, and handle duplicates if required.

5. Test with examples and analyze complexity

Walk through test cases like 'abcabcbb' and 'bbbbb'. Discuss time and space complexity for both parts, noting that the second part may be O(n^2) in worst case if many substrings exist.

Key Points to Mention

Sliding window technique with two pointers and a hash set/dictionary
Time complexity O(n) for the length, and space complexity O(min(n, m)) where m is alphabet size
Handling edge cases: empty string, all unique characters, all same characters
For the extension, consider using a list to collect substrings and avoid duplicates if necessary
Potential optimization: use an array for character indices if the character set is small (e.g., ASCII)
Discuss trade-offs: returning all substrings may increase space complexity and require careful handling of overlapping windows

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