Google·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Google SWE coding round, just one algorithmic problem about generating all subsets of an array. Pretty standard backtracking territory but easy to fumble if you haven't drilled it recently.

Questions Asked (1)

Given an array of unique integers, return all possible subsets (the power set), including the empty set.

Algorithms & Data Structures

Author's notes

I knew this one but still fumbled the initial setup.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and constraints, then discuss multiple approaches (recursive backtracking, iterative bit manipulation, or BFS) and their trade-offs. Choose one approach to implement, explaining your reasoning, and analyze time and space complexity.

Pro tip: Mention that the number of subsets is 2^n, so any algorithm must take at least O(2^n) time; this shows you understand the inherent complexity. Also, discuss how to handle duplicates if the array had them, demonstrating foresight.

1. Clarify the problem

Confirm that the array contains unique integers and that the output should include all subsets, including the empty set. Ask about input size constraints and whether the order of subsets matters.

2. Discuss possible approaches

Outline at least two methods: recursive backtracking (DFS), iterative bit manipulation, or BFS. Compare their time and space complexities and ease of implementation.

3. Choose and implement

Select one approach (e.g., backtracking) and write clean, bug-free code. Explain each step of the algorithm as you code.

4. Analyze complexity

State that the time complexity is O(n * 2^n) because there are 2^n subsets and each subset takes O(n) time to copy. Space complexity is O(n * 2^n) for the output, plus O(n) recursion stack.

5. Test with examples

Walk through a small example (e.g., [1,2,3]) to verify correctness and edge cases like empty array.

Key Points to Mention

Time complexity: O(n * 2^n) and why it's optimal
Space complexity: O(n * 2^n) for output and O(n) for recursion stack
Recursive backtracking approach: include/exclude each element
Iterative bit manipulation approach: use binary representation from 0 to 2^n - 1
Handling duplicates (if the array had duplicates) by sorting and skipping
Edge cases: empty array, single element, large n

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