Apple·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Apple DS technical screen, basically a coding question dressed up with a lot of follow-up requirements. They wanted the full explanation of why the approach works, not just a working solution, which I wasn't totally expecting.

Questions Asked (1)

Given a string and an integer k, find the length of the longest substring that can be made entirely of one character by replacing at most k characters. Explain your approach, prove why it works, and handle edge cases like k=0 and very large inputs.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The solution itself clicked pretty fast, sliding window with a running max frequency.

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AI HintsAI Generated

Suggested Approach

Use a sliding window (two-pointer) technique to maintain a window where the number of characters to replace (window length minus the count of the most frequent character) is at most k. Expand the right pointer, and when the condition is violated, shrink the window from the left. Track the maximum window length seen.

Pro tip: Emphasize that the window size never decreases, which allows O(n) time and O(1) space. Also, mention that this approach is optimal and can handle very large inputs efficiently.

1. Clarify the problem and edge cases

Restate the problem in your own words and ask clarifying questions. Discuss edge cases like k=0, empty string, k >= string length, and very large inputs.

2. Outline the sliding window approach

Explain that you'll maintain a window [left, right] and a frequency count of characters. The window is valid if (window length - max frequency) <= k. Expand right, and if invalid, move left.

3. Prove correctness and complexity

Argue that the window always represents a valid substring, and since we only expand when possible, the maximum length is found. Time complexity O(n), space O(1) (since alphabet size is fixed).

4. Handle edge cases and optimizations

For k=0, it reduces to finding the longest substring with all same characters. For very large inputs, the O(n) time and O(1) space make it efficient. Mention that the window size never decreases, so we can avoid shrinking below the current max.

5. Discuss trade-offs and alternatives

Compare with brute force O(n^2) or binary search with sliding window. Highlight that the sliding window is optimal and simple to implement.

Key Points to Mention

Sliding window technique with two pointers
Condition: window length - max frequency <= k
Time complexity O(n), space O(1) (fixed alphabet)
Edge cases: k=0, empty string, k >= length, all same characters
Proof: window always valid, and we only expand when possible, ensuring maximum length
Optimization: window size never decreases, so we can avoid unnecessary shrinking

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