Meta·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Meta coding round for a software engineer role. One design question about building a data structure with two operations, nothing too wild but it took me a bit to think through the sliding window angle.

Questions Asked (1)

Design a class that supports two operations: adding a value to a list, and computing the product of the last k elements.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Took me a second to realize the naive approach of just multiplying the last k elements each time is fine for small inputs but they clearly wanted something better.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements: the data type of values, the range of k, and whether k can exceed the current list size. Then propose a solution using a dynamic array (or list) to store elements and maintain a running product of the last k elements, updating it efficiently on each addition. Discuss trade-offs between time and space, and consider edge cases like k=0 or negative values.

Pro tip: Mention that you can optimize for O(1) time per operation by maintaining a product of the last k elements, but be prepared to handle division by zero if zeros are allowed. Alternatively, use a sliding window with a queue and recompute product when needed, showing awareness of the zero-handling trade-off.

1. Clarify requirements and constraints

Ask about the data type of values (integers, floats?), the range of k, whether k can be larger than the list size, and if negative numbers or zeros are allowed. This ensures you design the correct solution.

2. Choose data structures

Decide on a dynamic array (like Python list or Java ArrayList) to store elements, and possibly a queue or circular buffer to maintain the last k elements. Consider if you need to track the product separately.

3. Design the add operation

Append the new value to the list. Update the product of the last k elements: if the list size exceeds k, remove the oldest element's contribution (if using division) or recompute the product from the last k elements.

4. Design the getProduct operation

Return the precomputed product if maintained, or compute the product of the last k elements on the fly. Handle edge cases: if k > list size, return product of all elements; if k=0, return 1 (empty product).

5. Analyze time and space complexity

Discuss the trade-offs: maintaining a running product gives O(1) add and O(1) getProduct, but requires handling zeros. Recomputing on each getProduct gives O(k) time but simpler code. Space is O(n) for storing elements.

Key Points to Mention

Use a dynamic array to store elements and a separate variable to track the product of the last k elements.
Handle zeros carefully: if using division to update the product, division by zero is problematic; consider maintaining a count of zeros or recomputing when a zero is in the window.
Edge cases: k=0 (product should be 1), k > list size (product of all elements), negative numbers (product sign).
Time complexity: aim for O(1) per operation by updating the product incrementally, but acknowledge the O(k) recomputation approach as a simpler alternative.
Space complexity: O(n) for storing all elements, but if only the last k are needed, a circular buffer of size k can reduce space to O(k).
Discuss potential follow-ups: what if we need the product of the last k elements frequently? Could use a segment tree or prefix products for O(1) queries with O(n) preprocessing.

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