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

Intermediate
May 2026

Summary

Amazon SWE online assessment with a logistics simulation problem. Pretty straightforward premise but the greedy vs brute-force angle takes a second to see clearly.

Questions Asked (1)

Q1

Given a set of trucks each with a starting weight capacity, and a list of packages with given weights, determine if all packages can be delivered. Each time a truck makes a delivery, its capacity is halved (floor division). Return 1 if all packages can be delivered, 0 otherwise. Multiple test scenarios are provided.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

My first instinct was to just sort the packages descending and greedily assign them to the highest-capacity truck available.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then propose a greedy algorithm that assigns the heaviest package to the truck with the largest remaining capacity, halving that truck's capacity after each assignment. Analyze the time complexity and discuss potential optimizations or alternative approaches.

Pro tip: Mention that using a max-heap for truck capacities allows efficient retrieval of the largest capacity, and emphasize the importance of handling multiple test scenarios by resetting data structures between cases.

1. Clarify the problem

Ask clarifying questions to confirm assumptions: Are packages indivisible? Can trucks be used multiple times? Is the halving applied after each delivery? What are the constraints on number of trucks, packages, and weights?

2. Choose an algorithm

Propose a greedy strategy: sort packages in descending order, use a max-heap for truck capacities, and for each package, assign it to the truck with the largest capacity, then halve that capacity and reinsert into the heap.

3. Analyze complexity

State that the time complexity is O(P log T) where P is number of packages and T is number of trucks, due to heap operations, and space complexity is O(T) for the heap.

4. Handle edge cases

Discuss edge cases: no packages (return 1), no trucks (return 0 if packages exist), packages heavier than any initial capacity (return 0), and multiple test scenarios requiring reinitialization.

5. Discuss trade-offs

Compare greedy with other approaches like dynamic programming or backtracking, explaining why greedy is optimal here and its efficiency for large inputs.

Key Points to Mention

  • Greedy choice: always assign the heaviest remaining package to the truck with the largest current capacity.
  • Use a max-heap (priority queue) to efficiently get the truck with the largest capacity.
  • After each delivery, update the truck's capacity by integer division by 2 and reinsert into the heap.
  • Time complexity: O(P log T) where P is number of packages and T is number of trucks.
  • Edge cases: empty package list, no trucks, packages exceeding any truck's initial capacity.
  • Multiple test scenarios: ensure data structures are reset or reinitialized for each scenario.

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