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Coursera·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Coursera SWE interview that leaned pretty heavily into algorithm fundamentals. The coding problem looked simple at first glance but the majority vote part had a real constraint that tripped me up a bit.

Questions Asked (2)

Q1

Given an array of candidate votes, implement a plurality voting function that returns the candidate with the most votes, breaking ties by returning the lexicographically or numerically smallest candidate.

Algorithms & Data Structures
Author's notes

The counting part was fine, just a frequency map and a scan.

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AI HintsAI Generated

Suggested Approach

First, clarify the input format and tie-breaking rules, then propose an efficient solution using a hash map to count votes and track the best candidate. Discuss time and space complexity, and consider edge cases such as empty input or mixed types.

Pro tip: Mention that you can combine counting and comparison in a single pass to avoid a second iteration, and explicitly state how you handle ties to show attention to detail.

1. Clarify requirements

Ask about input size, data types, and tie-breaking rules (lexicographical vs numerical). Confirm expected output for edge cases like empty array or all ties.

2. Choose data structures

Use a hash map to count votes for each candidate. Maintain variables for the current winner and their count to avoid a second pass.

3. Implement single-pass algorithm

Iterate through votes, update counts, and compare with current winner. If count exceeds winner's count, update winner; if equal, apply tie-breaking rule.

4. Analyze complexity

State time complexity O(n) and space complexity O(k) where k is number of unique candidates. Discuss trade-offs if k is large.

5. Test with edge cases

Walk through examples: empty array, single candidate, tie between two, tie among many. Verify tie-breaking works as expected.

Key Points to Mention

  • Hash map for O(1) average vote counting
  • Single-pass approach to track winner and count
  • Tie-breaking logic: compare candidate identifiers when counts are equal
  • Time complexity O(n) and space complexity O(k)
  • Handling empty input or invalid votes gracefully
  • Clarifying assumptions about input types and tie-breaking rules

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

Q2

Implement a majority vote function using the Boyer-Moore algorithm that returns the candidate with strictly more than half the votes, or null if none exists, in O(n) time and O(1) space.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

This is where I actually struggled.

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AI HintsAI Generated

Suggested Approach

Start by explaining the Boyer-Moore voting algorithm: first pass finds a candidate by maintaining a count and switching candidates when count hits zero; second pass verifies if the candidate appears more than n/2 times. Emphasize that the algorithm guarantees a candidate if a majority exists, but verification is necessary to confirm. Then, walk through a concrete example to illustrate the process and discuss time/space complexity.

Pro tip: Mention that the algorithm can be extended to find elements appearing more than n/k times, showing deeper understanding. Also, note that the verification step is crucial because the candidate might not be the majority.

1. Understand the problem and constraints

Clarify that the function should return the majority element (appearing > n/2 times) or null if none exists, with O(n) time and O(1) space. Confirm that the input is an array of votes (e.g., integers or strings).

2. Explain the Boyer-Moore algorithm

Describe the two-phase approach: first, find a candidate by iterating through the array, maintaining a count and current candidate; second, verify the candidate's frequency by counting its occurrences.

3. Walk through an example

Use a small array (e.g., [A, B, A, A, C, A]) to demonstrate how the candidate and count evolve, and how verification confirms A as the majority.

4. Analyze complexity and edge cases

State that time complexity is O(n) (two passes) and space is O(1). Discuss edge cases: empty array, single element, no majority, and all elements same.

5. Implement the function

Write clean code with clear variable names, handling null/empty input, and returning null if no majority is found after verification.

Key Points to Mention

  • The algorithm works in two passes: candidate selection and verification.
  • During candidate selection, the count is incremented if the current element matches the candidate, otherwise decremented; if count reaches zero, the candidate is updated.
  • Verification is essential because the candidate may not be the majority element.
  • Time complexity is O(n) and space complexity is O(1).
  • The algorithm can be generalized to find elements appearing more than n/k times.
  • Edge cases: empty array, single element, no majority, and all elements identical.

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