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Amazon·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Amazon SWE coding round, basically a Top-K problem with some follow-up discussion on trade-offs. Pretty standard but the streaming variant caught me a bit flat-footed.

Questions Asked (1)

Q1

Given a large collection of items with associated scores or frequencies, return the K items with the highest values. Walk through your approach and discuss the trade-offs between different solutions.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

I started with the naive sort-and-slice answer, which they clearly expected, then moved to the min-heap approach.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (size of collection, value range, K relative to N, memory limits) and then present multiple solutions with increasing efficiency: full sort, min-heap of size K, and quickselect. Compare their time/space complexities and discuss when each is appropriate, emphasizing the heap approach for large N and small K.

Pro tip: Mention that for streaming data or when K is very small, a min-heap of size K is optimal, and for static data with large K, quickselect gives O(N) average time; also note that if values are bounded integers, counting sort can be even faster.

1. Clarify requirements and constraints

Ask about the size of the collection (N), the value range, whether K is much smaller than N, if the data is static or streaming, and memory limitations. This determines the best approach.

2. Propose baseline solutions

Describe simple approaches: sorting all items (O(N log N)) or using a max-heap (O(N + K log N)). Discuss their trade-offs in time and space.

3. Present optimized solutions

Introduce a min-heap of size K for O(N log K) time and O(K) space, ideal for large N and small K. Also mention quickselect for O(N) average time when K is large.

4. Compare trade-offs

Analyze time and space complexities, stability, and suitability for streaming vs. static data. Highlight that the min-heap is best for streaming and small K, while quickselect is best for static data and large K.

5. Discuss edge cases and optimizations

Cover cases like K > N, duplicate values, and bounded integer values (counting sort). Mention that for very small K, a simple insertion sort into a fixed-size array can be efficient.

Key Points to Mention

  • Time and space complexity of each approach: full sort O(N log N), max-heap O(N + K log N), min-heap O(N log K), quickselect O(N) average.
  • Min-heap of size K is optimal for large N and small K, and works well for streaming data.
  • Quickselect provides O(N) average time but O(N) space and is not suitable for streaming.
  • If values are bounded integers, counting sort or bucket sort can achieve O(N) time.
  • Edge cases: K > N, K = 0, duplicate values, and memory constraints.
  • Amazon leadership principles: customer obsession (clarify requirements), dive deep (analyze trade-offs), and deliver results (choose efficient solution).

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