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Amazon·Software Engineer·Online Assessment (OA)·Intermediate

Intermediate
Jun 2026

Summary

Amazon SWE online assessment with a scheduling/parallelism problem. Pretty classic OA format, nothing too surprising, but the problem had some nuance that I didn't fully think through at first.

Questions Asked (1)

Q1

Given an array of server request processing times and a maximum parallelism limit, find the minimum total processing time when no more than maxRequests requests can run simultaneously.

Algorithms & Data Structures
Author's notes

My first instinct was greedy, sort and batch.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then propose an efficient algorithm such as binary search on the answer combined with a greedy feasibility check. Explain the time and space complexity and discuss potential optimizations or alternative approaches.

Pro tip: Always discuss trade-offs between different approaches (e.g., binary search vs. priority queue) and mention how you would handle large inputs or real-world constraints like dynamic request arrivals.

1. Clarify the problem

Ask questions to confirm understanding: Are processing times integers? Can requests be split? Is the order fixed? What are the input size limits?

2. Identify the core challenge

Recognize that this is a scheduling problem to minimize makespan with a parallelism limit, which is NP-hard in general but may have efficient solutions for specific cases.

3. Propose an algorithm

For identical machines and arbitrary processing times, binary search on the minimum time T and check feasibility by greedily assigning requests to machines using a priority queue (simulating earliest available machine).

4. Analyze complexity

State that the binary search runs in O(log(sum(times))) iterations, each feasibility check takes O(n log k) with a heap, where n is number of requests and k is maxRequests.

5. Discuss edge cases and optimizations

Cover cases like maxRequests >= n (answer is max time), maxRequests = 1 (answer is sum), and mention potential optimizations like using a bucket queue if times are bounded.

Key Points to Mention

  • Problem can be modeled as scheduling on identical parallel machines to minimize makespan.
  • Binary search on the answer with a greedy feasibility check is a common and efficient approach.
  • Use a min-heap to track the earliest available machine when simulating the schedule.
  • Time complexity: O(n log k * log(sum(times))) and space complexity: O(k).
  • Edge cases: maxRequests >= n, maxRequests = 1, and zero processing times.
  • Alternative approaches: dynamic programming for small n, or approximation algorithms for large n.

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