Meta·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Meta SWE coding screen, one question on range sum queries. Pretty straightforward if you've seen prefix sums before, but I fumbled explaining the tradeoff between preprocessing and query time more than I'd like to admit.

Questions Asked (1)

Given an integer array and repeated range sum queries, design a class that preprocesses the array so each query runs in constant time.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I knew prefix sums going in, so the implementation wasn't the issue.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (array size, number of queries, value ranges) and then propose a prefix sum array to achieve O(1) query time with O(n) preprocessing. Explain the construction and query logic, and discuss trade-offs such as memory usage and handling updates.

Pro tip: Mention that prefix sums work for any associative operation with an inverse, but for non-invertible operations like min/max, you'd need a segment tree or sparse table. This shows you understand the underlying principles and can adapt to variations.

1. Clarify requirements and constraints

Ask about array size, number of queries, whether the array is static or dynamic, and the range of values to determine the best approach.

2. Propose prefix sum array

Explain that you'll precompute a prefix sum array where prefix[i] = sum of first i elements, enabling O(1) range sum queries.

3. Detail construction and query logic

Describe how to build the prefix array in O(n) and how to answer a query for range [l, r] as prefix[r+1] - prefix[l].

4. Discuss trade-offs and edge cases

Mention memory overhead, handling of large sums (overflow), and what happens if updates are required (then consider Fenwick tree or segment tree).

5. Implement the class

Outline the class structure with a constructor that preprocesses and a method that returns the sum for a given range in O(1).

Key Points to Mention

Prefix sum array construction in O(n) time and space.
O(1) query time by subtracting two prefix sums.
Handling 0-indexed vs 1-indexed arrays and inclusive/exclusive bounds.
Trade-off: prefix sums are ideal for static arrays; for dynamic updates, use Fenwick tree (BIT) or segment tree.
Potential integer overflow and use of appropriate data types (e.g., long).
Edge cases: empty array, l > r, out-of-bounds indices.

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