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Microsoft·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Microsoft SWE interview with an array filtering problem that looks straightforward until you hit the tie-breaking edge case. The problem itself was clean but the devil was in the details around duplicate handling.

Questions Asked (1)

Q1

Given a list of integers and a value k, return a new list containing only the k largest elements, preserving their original order. Duplicates count separately, and if values are tied at the boundary, keep the leftmost ones to hit exactly k elements.

Algorithms & Data Structures
Author's notes

The basic case I got pretty quickly.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then propose an efficient algorithm such as using a min-heap of size k to track the k largest elements while preserving order. After identifying the threshold value, scan the original list to collect exactly k elements, handling ties by taking leftmost occurrences.

Pro tip: Discuss the trade-off between time and space complexity, and mention that a heap-based solution is optimal for large lists when k is small, but a sorting-based approach might be simpler if k is close to the list size.

1. Clarify requirements and edge cases

Ask about input size, whether k can be 0 or exceed list length, and if the list can be empty. Confirm that duplicates count separately and ties are broken by leftmost occurrence.

2. Choose an efficient algorithm

Decide between approaches: sorting the list (O(n log n)) or using a min-heap of size k (O(n log k)). For large n and small k, the heap is better; otherwise, sorting may be simpler.

3. Identify the k-th largest value

Use the chosen method to find the threshold value that separates the k largest elements. If using a heap, after processing all elements, the heap's minimum is the threshold.

4. Collect exactly k elements preserving order

Scan the original list from left to right, adding elements greater than the threshold, and for elements equal to the threshold, add only as many as needed to reach k, taking the leftmost ones.

5. Analyze complexity and test

State the time and space complexity of your solution. Walk through edge cases like k=0, k=n, all elements equal, and duplicates at the boundary.

Key Points to Mention

  • Time and space complexity trade-offs between sorting and heap-based approaches
  • Handling of duplicates and ties at the boundary by taking leftmost occurrences
  • Edge cases: k=0, k >= list length, empty list, all elements equal
  • Preservation of original order in the output list
  • Use of a min-heap to efficiently find the k largest elements
  • Potential for a quickselect-based approach to find the threshold in O(n) average time

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