Amazon·Frontend Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Amazon frontend interview with a coding round that leaned more algorithmic than I expected. The question was about log parsing, which sounds simple enough, but they pushed pretty hard on the optimal solution.

Questions Asked (1)

You're given an array of logs, each with a status (success or failure) and a timestamp. Failures are monotonic, meaning once a failure appears, everything after it is also a failure. Find the timestamp of the first failure, or return that no failure exists. Walk through both a linear scan and a binary search solution.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I went straight to the linear scan from the end, which works fine but is O(n) and I kind of knew they'd push back.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, then explain the linear scan solution as a baseline. Next, introduce binary search by leveraging the monotonic property of failures to achieve O(log n) time, and discuss trade-offs between the two approaches.

Pro tip: Emphasize that binary search is only valid because failures are monotonic; without that guarantee, you'd need a linear scan. Also, mention that in a real-world frontend context, you might debounce or batch logs to avoid performance bottlenecks.

1. Clarify the problem

Ask about input format, size, and whether the array is sorted by timestamp. Confirm that failures are monotonic and that you need the first failure's timestamp.

2. Linear scan solution

Iterate through the logs from start to end, returning the timestamp of the first failure encountered. If none, return a sentinel value like -1 or null.

3. Binary search solution

Use binary search to find the leftmost failure. Maintain low and high pointers, check the middle element: if it's a failure, move high to mid; else move low to mid+1. After the loop, check if low is within bounds and points to a failure.

4. Compare trade-offs

Discuss time and space complexity: linear scan is O(n) time and O(1) space; binary search is O(log n) time and O(1) space. Mention that binary search requires the monotonic property and random access, which arrays provide.

5. Handle edge cases

Consider empty array, all successes, all failures, and single-element array. Ensure the binary search correctly handles these cases.

Key Points to Mention

Monotonic property of failures enables binary search
Time complexity: O(n) vs O(log n)
Space complexity: O(1) for both
Edge cases: empty array, no failures, all failures
Binary search implementation details: leftmost occurrence, loop termination condition
Real-world application: efficient log processing in frontend monitoring

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