Snapchat·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Snapchat SWE interview with a combinatorics problem that looks straightforward until you actually have to implement it cleanly. The recursive angle was the main focus.

Questions Asked (1)

Given a list of keys and a mapping from each key to a list of values, and a query subset of those keys, return all combinations formed by picking exactly one value per key in order. For example, querying keys [2, 3] from a map like {1: ['a','b'], 2: ['c','d'], 3: ['e','f']} should produce [['c','e'],['c','f'],['d','e'],['d','f']]. Solve it recursively or iteratively using a queue or stack.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

My first instinct was itertools.product and call it a day, but they wanted an actual implementation.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then present both recursive and iterative solutions. Explain the recursive backtracking approach and the iterative BFS using a queue, analyzing time and space complexity. Discuss trade-offs and potential optimizations.

Pro tip: Mention that the iterative BFS approach naturally handles large outputs without recursion depth limits, and that you can optimize by precomputing the Cartesian product size to allocate memory efficiently.

1. Clarify requirements and edge cases

Ask about input size, key order, duplicate keys, empty values, and whether the query keys are guaranteed to exist. Confirm output format.

2. Outline recursive solution

Describe a backtracking function that builds combinations by iterating over values for the current key and recursing to the next key. Base case: when all keys processed, add the current combination to results.

3. Outline iterative solution

Use a queue (BFS) to build combinations level by level: start with an empty combination, for each key, dequeue each partial combination and enqueue extended combinations with each value. After processing all keys, the queue contains the final combinations.

4. Analyze complexity and trade-offs

Time complexity is O(N * M) where N is the number of combinations and M is the number of keys (or total output size). Space complexity is O(N * M) for storing results. Recursive uses call stack; iterative uses queue. Discuss which is better for large inputs.

5. Discuss optimizations and extensions

Mention precomputing total combinations to reserve space, handling missing keys gracefully, and potential parallelization or lazy evaluation if not all combinations are needed.

Key Points to Mention

Recursive backtracking with base case and iteration over values
Iterative BFS using a queue to build combinations level by level
Time and space complexity analysis: O(N * M) where N is product of value counts and M is number of keys
Handling edge cases: empty query, missing keys, empty value lists
Trade-offs between recursion (simpler, but stack overflow risk) and iteration (more memory but no stack limit)
Potential optimization: precompute total combinations to allocate result array efficiently

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