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Microsoft·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Microsoft coding screen for a software engineer role, one question about reconstructing a DNA sequence from linked fragments. Pretty straightforward once you see the structure, but I fumbled the setup a bit before getting there.

Questions Asked (1)

Q1

You're given a list of DNA fragment objects, each with a start ID, end ID, and a payload string. The fragments form a single chain where one fragment's end ID matches the next one's start ID. Write a function that reconstructs the full DNA sequence by chaining and concatenating the payloads in order.

Algorithms & Data Structures
Author's notes

My first instinct was to sort, which was wrong.

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AI HintsAI Generated

Suggested Approach

Model the fragments as a directed graph where each fragment is an edge from start ID to end ID, then find the Eulerian path that visits every edge exactly once. Since the fragments form a single chain, you can also use a hash map from start ID to fragment and traverse from the unique start node (one with no incoming edges) to reconstruct the sequence in O(n) time.

Pro tip: Clarify whether the chain is guaranteed to be linear and complete; if so, the hash map approach is simpler and faster than a full Eulerian path algorithm. Mention that you would validate the input to handle edge cases like duplicate start IDs or cycles.

1. Understand the problem and constraints

Confirm that fragments form a single chain with no branching, and that each fragment's end ID matches the next fragment's start ID. Identify the unique start fragment (start ID not appearing as any end ID) and end fragment (end ID not appearing as any start ID).

2. Choose the right data structure

Use a hash map (dictionary) to map each start ID to its corresponding fragment object, enabling O(1) lookup. Alternatively, build an adjacency list if the graph might have branches, but for a single chain, the hash map is sufficient.

3. Reconstruct the sequence

Start from the unique start fragment, append its payload to the result, then follow the chain by looking up the next fragment using the current fragment's end ID. Continue until no further fragment exists.

4. Handle edge cases and validate

Check for empty input, single fragment, or invalid chains (e.g., missing links, cycles). If the chain is not guaranteed, consider using an Eulerian path algorithm to handle general cases.

5. Analyze complexity and test

State that the time complexity is O(n) for building the map and traversing the chain, and space complexity is O(n). Walk through a small example to verify correctness.

Key Points to Mention

  • Graph modeling: fragments as edges, IDs as nodes
  • Hash map for O(1) lookups to chain fragments efficiently
  • Identifying the start node (no incoming edges) and end node (no outgoing edges)
  • Time and space complexity: O(n) time, O(n) space
  • Edge cases: empty list, single fragment, invalid chain, cycles
  • Alternative approach: Eulerian path if the chain is not guaranteed to be linear

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