← Oracle Interview Insights

Oracle·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Oracle SWE interview with a classic greedy/DP problem. Nothing too surprising but it's the kind of question that feels easy until you're actually in it.

Questions Asked (1)

Q1

Given an array of non-negative integers where each element represents the maximum jump length from that position, determine whether you can reach the last index starting from the first.

Algorithms & Data Structures
Author's notes

I went with the greedy approach, tracking the farthest reachable index as I walked through the array.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and edge cases, then explain a greedy algorithm that tracks the farthest reachable index. Walk through the algorithm with a small example, analyze time and space complexity, and discuss potential optimizations or alternative approaches.

Pro tip: Emphasize the greedy choice: at each step, update the farthest reachable index and check if it covers the current position. This demonstrates you can identify optimal substructure and avoid unnecessary computation.

1. Clarify and Validate

Restate the problem in your own words and confirm assumptions: non-negative integers, empty array, single element, etc. Ask clarifying questions if needed.

2. Outline Approach

Propose a greedy algorithm: iterate through the array, maintaining the farthest index reachable so far. If the current index exceeds the farthest, return false; otherwise update farthest with max(farthest, i + nums[i]).

3. Walk Through Example

Trace the algorithm on a small example (e.g., [2,3,1,1,4]) to show how it works and builds confidence.

4. Analyze Complexity

State that the algorithm runs in O(n) time and O(1) space, which is optimal for this problem.

5. Discuss Alternatives and Edge Cases

Mention dynamic programming or BFS as alternatives, but highlight that greedy is most efficient. Cover edge cases like empty array, single element, and zeros.

Key Points to Mention

  • Greedy algorithm with farthest reachable index
  • Time complexity O(n) and space complexity O(1)
  • Handling edge cases: empty array, single element, zeros
  • Proof of correctness: invariant that farthest is the maximum reachable index
  • Comparison with dynamic programming (O(n^2)) and BFS
  • Early termination when farthest >= last index

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