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Google·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Jun 2026

Summary

Google SWE coding round, one question but it had enough depth to keep me busy for a while. Basically a follow-up to the classic subsets problem, except with duplicates in the input which changes everything about how you approach it.

Questions Asked (1)

Q1

Given an integer array that may contain duplicate elements, return all possible subsets with no duplicate subsets in the result.

Algorithms & Data Structures
Author's notes

My first instinct was to just slap a set on the output from the plain subsets solution and call it done.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and constraints, then propose a backtracking solution that sorts the array and skips duplicates at each recursion level to avoid generating duplicate subsets. Analyze the time and space complexity, and discuss potential optimizations or alternative approaches.

Pro tip: Demonstrate awareness of the trade-offs between sorting and using a hash set for deduplication, and mention how the solution scales with input size. Also, proactively discuss edge cases like empty array and all duplicates.

1. Clarify the problem and constraints

Ask about input size, range of integers, and whether the output order matters. Confirm that subsets are combinations, not permutations, and that duplicates in the input can lead to duplicate subsets if not handled.

2. Outline the backtracking approach

Explain that you will sort the array to bring duplicates together, then use a recursive backtracking function that builds subsets incrementally. At each step, skip over duplicate elements to avoid generating the same subset multiple times.

3. Detail the algorithm and handle duplicates

Describe the recursion: start with an empty subset, iterate through the array from a given index, and for each element, include it and recurse. To skip duplicates, if the current element equals the previous and the previous was not included in the current path, skip it. Alternatively, use a set to track seen elements at each recursion level.

4. Analyze complexity and edge cases

State that the time complexity is O(2^n) in the worst case (when all elements are unique), but with duplicates it's bounded by the number of unique subsets. Space complexity is O(n) for recursion depth plus O(2^n) for output. Discuss edge cases: empty array, all elements identical, and large n.

5. Discuss optimizations and alternatives

Mention that sorting is O(n log n) and enables efficient duplicate skipping. Alternatively, use a hash set to avoid sorting, but that may increase space. Also, consider iterative solution using bit manipulation for unique elements, but backtracking is more straightforward for duplicates.

Key Points to Mention

  • Sorting the array to group duplicates together
  • Backtracking with a 'start' index to avoid permutations
  • Duplicate skipping condition: if i > start and nums[i] == nums[i-1], continue
  • Time complexity: O(2^n) for unique elements, but output-sensitive with duplicates
  • Space complexity: O(n) recursion stack plus O(2^n) for result storage
  • Edge cases: empty input, all duplicates, and large input sizes

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