Amazon·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Amazon SWE interview with a classic efficiency question that sounds straightforward until you actually start talking through it out loud.

Questions Asked (1)

How would you efficiently compute the sum of a very large collection of integers?

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

My first instinct was to just say iterate and add, which felt embarrassingly simple.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the constraints: what 'very large' means (size, memory, distribution), whether the integers fit in memory, and if the data is static or streaming. Then propose a scalable solution, such as parallel processing with map-reduce or chunked summation, and discuss trade-offs like memory usage, speed, and accuracy (e.g., overflow, floating-point).

Pro tip: Demonstrate awareness of real-world constraints: mention that for truly massive data, you might use distributed frameworks like Apache Spark or Hadoop, but also consider simple optimizations like using 64-bit integers to avoid overflow and leveraging SIMD instructions for in-memory arrays.

1. Clarify Requirements

Ask about the size of the collection, memory limits, data source (file, stream, distributed), and whether the sum must be exact or approximate. This shows you avoid assumptions.

2. Consider In-Memory Solutions

If data fits in memory, discuss efficient summation using loops, built-in functions, or parallel reduction. Mention using 64-bit integers to prevent overflow.

3. Scale Out for Large Data

If data doesn't fit in memory, propose chunking, external sorting, or distributed processing (e.g., MapReduce). Explain how to combine partial sums.

4. Address Trade-offs

Compare approaches on speed, memory, complexity, and accuracy. For example, parallelization adds overhead but speeds up computation; streaming uses constant memory but may be slower.

5. Optimize and Validate

Suggest optimizations like using SIMD, avoiding unnecessary data copies, and validating with test cases. Mention handling edge cases like empty input or integer overflow.

Key Points to Mention

Time and space complexity of different approaches (e.g., O(n) sequential vs. O(n/p) parallel).
Integer overflow and the need for 64-bit accumulators or arbitrary-precision arithmetic.
Parallelization strategies: map-reduce, divide-and-conquer, or using multiple threads/processes.
Memory constraints: in-memory vs. external storage, streaming algorithms.
Distributed computing frameworks like Hadoop, Spark, or MPI for cluster-scale data.
Trade-offs between exact and approximate sums (e.g., using floating-point for speed but risking precision).

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