Amazon·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Apr 2026

Summary

Amazon SWE interview with a classic hard linked-list problem. Nothing shocking about the question itself but the complexity discussion at the end is where they really push you.

Questions Asked (1)

You have k sorted linked lists. Merge them all into a single sorted linked list and return it. Also walk through the time and space complexity of your solution.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I went with the min-heap approach pretty quickly, which was fine, but the complexity breakdown is where I fumbled a bit.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., k and list sizes) and then present a min-heap based solution that repeatedly extracts the smallest node and appends it to the result. After implementing, analyze time and space complexity, and discuss trade-offs with alternative approaches like divide-and-conquer.

Pro tip: At Amazon, interviewers value scalability and trade-off analysis. Explicitly compare the heap approach (O(N log k)) with the divide-and-conquer approach (O(N log k) but often faster in practice) and mention how you'd handle edge cases like empty lists or very large k.

1. Clarify requirements and constraints

Ask about the range of k, list lengths, memory limits, and whether the input lists can be modified. This shows you consider practical scenarios and helps tailor your solution.

2. Propose a min-heap solution

Explain that you'll use a min-heap of size k to efficiently select the smallest current node among the heads of the k lists. Initialize the heap with the head of each non-empty list.

3. Walk through the algorithm

Describe the loop: pop the smallest node from the heap, append it to the merged list, and if that node has a next, push it into the heap. Continue until the heap is empty.

4. Analyze time and space complexity

State that time complexity is O(N log k) where N is total number of nodes, because each node is pushed and popped once, each operation O(log k). Space complexity is O(k) for the heap (plus O(1) extra if reusing nodes).

5. Discuss trade-offs and alternatives

Mention that a divide-and-conquer approach merging pairs of lists also achieves O(N log k) time but may have better cache performance and lower constant factors. Also note that if k is small, a simple sequential merge might be acceptable.

Key Points to Mention

Time complexity: O(N log k) where N is total nodes and k is number of lists.
Space complexity: O(k) for the heap (or O(1) if using divide-and-conquer with in-place merging).
Use of a min-heap (priority queue) to efficiently track the smallest current node.
Handling edge cases: empty input, lists of different lengths, k=0 or k=1.
Comparison with alternative approaches: divide-and-conquer, sequential merging, and their trade-offs.
Potential optimization: if lists are very large, consider external sorting or distributed processing.

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