Amazon·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jul 2026Remote

Summary

Amazon SWE coding round, one question the whole time, and it was basically a streaming twist on a sliding window anagram problem. Not brutal, but the edge cases will get you if you're not careful.

Questions Asked (1)

Given a continuous stream of characters and a fixed target word, detect and report every position in real time where the last N characters (N = length of target) form an anagram of that target.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I knew the static version of this problem pretty well, but the streaming interface tripped me up more than I expected.

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AI HintsAI Generated

Suggested Approach

Use a sliding window of size N over the character stream, maintaining a frequency count of the window. Compare the window's frequency count to the target's frequency count in O(1) time per step by tracking the number of matching characters or using a rolling hash. Report the position whenever the counts match.

Pro tip: Mention that you can optimize by precomputing the target's frequency array and using a difference counter to avoid full comparisons. Also, discuss how to handle Unicode or case sensitivity if relevant.

1. Clarify requirements and constraints

Confirm the definition of 'position' (e.g., index of the last character of the window), whether the stream is infinite, and if characters are ASCII or Unicode. Ask about memory constraints and expected throughput.

2. Choose data structures

Use a fixed-size frequency array (e.g., size 256 for ASCII) for the target and the sliding window. Alternatively, use a hash map for Unicode. Maintain a counter of how many characters currently match the target's frequencies.

3. Design the sliding window algorithm

Initialize the window with the first N characters. For each new character, add it to the window and remove the oldest character. Update the match counter incrementally. If the match counter equals the number of distinct characters in the target, report the current position.

4. Analyze complexity and trade-offs

Time complexity is O(1) per character (amortized), total O(M) for M characters. Space is O(1) for fixed alphabet. Discuss alternative approaches like sorting the window (O(N log N) per step) and why they are inefficient.

5. Handle edge cases and extensions

Consider N=0, target longer than stream, repeated characters, and case-insensitivity. Discuss how to extend to multiple target words or overlapping anagrams.

Key Points to Mention

Sliding window technique with fixed size N
Frequency counting using arrays or hash maps
Incremental update of match count to achieve O(1) per character
Time and space complexity analysis
Handling of edge cases such as empty target or stream shorter than N
Trade-offs between different approaches (e.g., sorting vs. frequency counting)

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