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Apple·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

Apple data engineer interview, got a pretty classic coding question about primes. Nothing too wild but I fumbled the optimization part more than I'd like to admit.

Questions Asked (1)

Q1

Write a function that returns all prime numbers up to a given number n.

Algorithms & Data Structures
Author's notes

Started with the naive approach, checking every number up to n by dividing it by all smaller numbers.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem: confirm whether n is inclusive, the expected input range, and if the result should be a list or array. Then explain the Sieve of Eratosthenes algorithm, which is the optimal approach for generating all primes up to n, and discuss its time and space complexity. Finally, walk through a code implementation, handling edge cases like n < 2.

Pro tip: At Apple, interviewers value clean, efficient code and clear communication. Mention that the Sieve of Eratosthenes is the standard for this problem, but also briefly note the trade-offs with a trial division approach for very small n or memory-constrained environments.

1. Clarify requirements

Ask if n is inclusive, the expected input range, and the desired output format (e.g., list, array). Confirm edge cases like n < 2.

2. Choose algorithm

Select the Sieve of Eratosthenes for optimal O(n log log n) time complexity. Briefly mention alternative approaches like trial division and their trade-offs.

3. Explain algorithm steps

Describe how the sieve works: create a boolean array of size n+1, mark multiples of each prime starting from 2, and collect unmarked numbers.

4. Implement code

Write clean code, handling edge cases (n < 2 returns empty list). Use efficient loops and avoid unnecessary operations.

5. Analyze complexity and test

State time and space complexity (O(n log log n) time, O(n) space). Walk through a small example (e.g., n=10) to verify correctness.

Key Points to Mention

  • Sieve of Eratosthenes algorithm and its efficiency
  • Time complexity O(n log log n) and space complexity O(n)
  • Edge cases: n < 2, n = 2, large n
  • Alternative approaches like trial division and their trade-offs
  • Code clarity and readability
  • Potential optimizations: only marking odd numbers, using bitset for memory

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