Apple·Frontend Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026

Summary

Apple frontend interview with a coding problem that felt more CS fundamentals than frontend. The question wasn't crazy hard but the constraints made you think twice about your approach.

Questions Asked (1)

Given an unsorted integer array of roughly 75 elements that may contain duplicates, zero out all duplicate values keeping only the first occurrence of each, then sort the resulting array without using any built-in sort functions. Explain the time and space complexity of your solution.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The deduplication part clicked pretty fast.

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AI HintsAI Generated

Suggested Approach

First, clarify the requirements and constraints, then propose a two-phase solution: deduplicate while preserving the first occurrence, and sort the deduplicated array using a custom sorting algorithm like quicksort or mergesort. Explain the time and space complexity of each phase and the overall solution.

Pro tip: Mention that for small arrays (75 elements), even an O(n^2) sort like insertion sort is acceptable, but demonstrating awareness of O(n log n) algorithms shows depth. Also, discuss trade-offs between using extra space for a hash set versus in-place deduplication.

1. Clarify Requirements

Confirm with the interviewer whether the array can be modified in-place, if extra space is allowed, and if the order of first occurrences must be preserved before sorting.

2. Deduplicate

Iterate through the array, using a hash set to track seen values. Keep the first occurrence of each value and mark subsequent duplicates as zero (or remove them).

3. Sort Without Built-in Functions

Implement a sorting algorithm such as quicksort or mergesort on the deduplicated array. Explain the choice based on time/space trade-offs.

4. Analyze Complexity

State the time complexity: O(n) for deduplication and O(m log m) for sorting, where m is the number of unique elements. Space complexity: O(m) for the hash set and O(log m) to O(m) for the sort, depending on the algorithm.

5. Test and Optimize

Walk through edge cases (all duplicates, no duplicates, already sorted) and discuss potential optimizations or alternative approaches.

Key Points to Mention

Time complexity: O(n) for deduplication with a hash set, O(m log m) for sorting (e.g., quicksort/mergesort), overall O(n + m log m).
Space complexity: O(m) for the hash set, plus O(log m) to O(m) for the sorting algorithm's recursion/auxiliary space.
Choice of sorting algorithm: quicksort (in-place, average O(n log n), worst O(n^2)) vs. mergesort (stable, O(n log n) time, O(n) space).
Handling duplicates: zeroing out duplicates while preserving first occurrence, and ensuring zeros are not treated as duplicates if zero is a valid value.
Edge cases: empty array, all elements duplicates, already sorted array, and array with many duplicates.
Trade-offs: using extra space for hash set vs. in-place deduplication with O(n^2) time; stability of sort if order matters.

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