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Arista·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Jun 2026

Summary

Coding interview for a software engineer role at Arista. Two problems, both with complexity analysis required. Nothing too wild but the binary search follow-up on the second problem is where things get interesting.

Questions Asked (2)

Q1

Write a function to check if a string is a palindrome. Handle null input and treat an empty string as a valid palindrome.

Algorithms & Data Structures
Author's notes

Pretty standard.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements: null input should return false, empty string is a valid palindrome. Then implement a two-pointer approach comparing characters from both ends, ignoring case and non-alphanumeric characters if specified. Discuss time and space complexity, and consider edge cases like single character and strings with only non-alphanumeric characters.

Pro tip: Demonstrate awareness of Unicode and locale-specific characters, and mention that in production code you might normalize the string first. Also, proactively discuss the trade-off between the two-pointer approach and reversing the string, highlighting the O(1) space advantage of two-pointer.

1. Clarify requirements and edge cases

Confirm handling of null (return false), empty string (return true), and whether to ignore case and non-alphanumeric characters. Ask about input constraints and expected character set.

2. Choose an approach

Select the two-pointer technique for O(n) time and O(1) space, or string reversal for simplicity. Explain why two-pointer is more efficient in terms of space.

3. Implement the solution

Write clean code: handle null and empty string first, then use two pointers moving inward, comparing characters (optionally normalizing case and skipping non-alphanumeric).

4. Test with edge cases

Walk through examples: null, empty string, single character, 'racecar', 'A man, a plan, a canal: Panama', and strings with mixed case or punctuation.

5. Analyze complexity and discuss optimizations

State time complexity O(n) and space complexity O(1) for two-pointer. Mention potential optimizations like early exit on mismatch and handling Unicode normalization.

Key Points to Mention

  • Null input handling: return false as per requirement.
  • Empty string is a valid palindrome: return true.
  • Two-pointer technique for O(n) time and O(1) space.
  • Case insensitivity and ignoring non-alphanumeric characters (if required).
  • Edge cases: single character, all non-alphanumeric, Unicode characters.
  • Time and space complexity analysis.

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

Q2

Given a sorted array of distinct integers forming a consecutive sequence with exactly one missing number (sequence doesn't necessarily start at 0), find the missing number. First explain an O(n) solution, then optimize to O(log n) using binary search.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

The linear scan is easy: just walk through and find where the gap is.

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AI HintsAI Generated

Suggested Approach

Start by explaining the O(n) solution: compute the expected sum of the consecutive sequence and subtract the actual sum to find the missing number. Then, for O(log n), use binary search to find the first index where the value deviates from the expected value (index + offset), leveraging the sorted property.

Pro tip: Clarify that the sequence is consecutive and sorted, so the difference between the value and its index is constant until the missing number, then increases by 1. This insight is key to the binary search solution.

1. Understand the problem

Restate the problem: given a sorted array of distinct integers forming a consecutive sequence with one missing number, find the missing number. Note that the sequence may not start at 0, so determine the starting value.

2. O(n) solution

Explain that you can compute the expected sum of the full sequence using the arithmetic series formula, then subtract the sum of the given array. The difference is the missing number. This takes O(n) time and O(1) space.

3. Identify the pattern for binary search

Observe that in the complete sequence, the difference between each element and its index is constant (equal to the starting value). With one missing number, this difference remains constant until the missing number, then increases by 1 for all subsequent elements.

4. Implement binary search

Use binary search to find the first index where the difference changes. Compare the difference at the middle index with the expected difference. If it's the same, the missing number is to the right; otherwise, it's to the left. Return the missing number as the value at the found index minus 1 (or based on the pattern).

5. Analyze complexity and edge cases

State that the binary search solution runs in O(log n) time and O(1) space. Discuss edge cases: missing number at the beginning or end, array of size 1, and ensure the algorithm handles them correctly.

Key Points to Mention

  • The O(n) solution using sum formula: expected sum = (first + last) * n / 2, missing = expected sum - actual sum.
  • The key observation for binary search: in the complete sequence, arr[i] - i is constant; with one missing, it increases by 1 after the missing number.
  • Binary search condition: if arr[mid] - mid == expected difference, then missing is to the right; else to the left.
  • Time and space complexity: O(n) vs O(log n) time, O(1) space for both.
  • Edge cases: missing number at start or end, array length 1, and handling the starting value correctly.
  • Trade-offs: O(n) solution is simpler and may be preferred for small arrays or when code simplicity is prioritized; O(log n) is optimal for large arrays.

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