Google·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Google SWE interview with a classic array problem. Nothing too wild but the pressure of it being Google makes even straightforward questions feel heavier than they should.

Questions Asked (1)

Given an array of integers and a target value, find whether any combination of elements sums to that target.

Algorithms & Data Structures

Author's notes

Went with two pointers after sorting, which works for pairs but I fumbled when they pushed on subsets.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem: whether elements can be reused, if the array is sorted, and if we need to return the actual combination or just a boolean. Then discuss multiple approaches, from brute-force to dynamic programming, and analyze their time and space complexities. Finally, implement the most efficient solution, such as using a hash set for the 2-sum variant or DP for the general subset sum problem.

Pro tip: Always ask clarifying questions before diving into code; it shows you think about edge cases and requirements. Also, mention that for large targets or arrays, a meet-in-the-middle approach can be more efficient than DP.

1. Clarify the problem

Ask about constraints: Can elements be used multiple times? Is the array sorted? What are the size limits? Do we need to return the combination or just a boolean?

2. Discuss possible approaches

Outline brute-force, recursive backtracking, dynamic programming (subset sum), and specialized approaches for 2-sum or 3-sum. Compare their time and space complexities.

3. Choose and justify an approach

Select the most appropriate algorithm based on constraints, and explain why it's optimal. For example, use a hash set for 2-sum, or DP for general subset sum with small target.

4. Implement the solution

Write clean code, handling edge cases like empty array, negative numbers, and target zero. Use appropriate data structures and optimize space if possible.

5. Test and analyze

Walk through examples, test edge cases, and state the time and space complexity of your solution. Discuss potential improvements or trade-offs.

Key Points to Mention

Time and space complexity of each approach (e.g., O(n*T) for DP, O(n) for 2-sum with hash set).
Handling of negative numbers and zero target.
Whether elements can be reused (unbounded knapsack vs 0/1 knapsack).
Optimizations like early termination, pruning, or meet-in-the-middle for large inputs.
Edge cases: empty array, no solution, multiple solutions, large target.
Difference between subset sum (any combination) and contiguous subarray sum.

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