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Pinduoduo·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Pinduoduo SWE interview with a classic greedy array problem. Nothing too wild but the gap between the naive and optimal solution is exactly what they're testing for.

Questions Asked (1)

Q1

Given a 0-indexed array of non-negative integers, you start at index 0 and can jump forward up to nums[i] steps from index i. What is the minimum number of jumps to reach the last index?

Algorithms & Data Structures
Author's notes

I started with the DP approach because it felt safer to code quickly.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, then propose a greedy BFS approach that tracks the current jump range and the farthest reachable index. Explain how you update the jump count when you exhaust the current range, ensuring O(n) time and O(1) space.

Pro tip: Emphasize that the greedy approach works because the problem guarantees you can always reach the last index, and mention that you can optimize by stopping early if the farthest reachable index covers the last index. This shows you think about practical optimizations and understand the problem deeply.

1. Clarify constraints and edge cases

Ask about input size, whether the last index is always reachable, and handle edge cases like array length 0 or 1. This ensures you understand the problem fully before coding.

2. Propose a greedy BFS strategy

Explain that you can treat the array as levels in a BFS where each level represents the range of indices reachable with the current number of jumps. Track the current level's end and the farthest index reachable from the current level.

3. Walk through the algorithm

Iterate through the array, updating the farthest reachable index. When you reach the end of the current jump range, increment the jump count and set the new range end to the farthest reachable index. Stop when the range covers the last index.

4. Analyze complexity and edge cases

State that the algorithm runs in O(n) time and O(1) space. Discuss how it handles cases like all zeros (except last) or large jumps, and confirm it returns the minimum jumps.

5. Test with examples

Walk through a small example (e.g., [2,3,1,1,4]) to demonstrate the algorithm step by step, showing how the jump count is updated and the result is obtained.

Key Points to Mention

  • Greedy BFS approach with O(n) time and O(1) space
  • Tracking current jump range and farthest reachable index
  • Incrementing jump count when the current range is exhausted
  • Handling edge cases: empty array, single element, unreachable last index (though problem guarantees reachability)
  • Early termination when the farthest reachable index covers the last index
  • Comparison with dynamic programming (O(n^2)) to highlight efficiency

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