Amazon·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026

Summary

Amazon SWE coding round, pretty standard stuff. One array question that sounds trivial but they actually want you to talk through the tradeoffs between the two main approaches, not just code one up and call it done.

Questions Asked (1)

Given an integer array, return true if any value appears more than once, and false if all elements are distinct. Walk through multiple approaches and their complexity tradeoffs.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The coding part is easy, a hash set and you're done in five lines.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., array size, value range, memory limits) and then present multiple solutions with increasing efficiency: brute force, sorting, and hash set. For each, analyze time and space complexity, and discuss trade-offs to demonstrate a deep understanding of algorithmic design.

Pro tip: At Amazon, interviewers value the ability to optimize for real-world scenarios. After presenting the optimal solution, mention how you would handle edge cases like very large arrays that don't fit in memory, or if the input is a stream, showing you think beyond the basic algorithm.

1. Clarify requirements and constraints

Ask about input size, value range, memory limits, and whether the array can be modified. This shows you consider practical constraints before diving into solutions.

2. Present brute force approach

Describe the naive O(n^2) solution using nested loops to check each pair. Mention its simplicity but poor scalability for large inputs.

3. Present sorting approach

Explain that sorting the array first (O(n log n)) allows checking adjacent elements for duplicates. Discuss that this modifies the input and may be slower than optimal but uses less extra space.

4. Present hash set approach

Describe using a hash set to track seen elements, achieving O(n) time and O(n) space. Highlight that this is typically the optimal solution for general cases.

5. Compare trade-offs and discuss edge cases

Summarize the time and space complexity of each approach, and discuss when each might be preferred (e.g., memory constraints, input size). Mention edge cases like empty array, single element, or very large arrays.

Key Points to Mention

Time and space complexity analysis for each approach (brute force: O(n^2) time, O(1) space; sorting: O(n log n) time, O(1) or O(n) space depending on sort; hash set: O(n) time, O(n) space).
Trade-offs between time and space: hash set is fastest but uses extra memory; sorting is slower but may use less memory if in-place sort is allowed.
Edge cases: empty array, single element, all elements distinct, all elements same, and large arrays that may not fit in memory.
Potential optimizations: early exit when a duplicate is found, using a bitset if the value range is small, or using a Bloom filter for approximate detection in streaming scenarios.
Real-world considerations: if the array is sorted, the problem reduces to checking adjacent elements; if the array is immutable, sorting may not be an option.
Communication: clearly explain the reasoning behind each approach and justify the final recommendation based on the constraints.

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