Adobe·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jul 2026

Summary

Adobe SWE interview with a dynamic programming problem that looks straightforward until you start thinking about the bonus condition. The question had enough moving parts to trip you up if you're not careful with your state transitions.

Questions Asked (1)

You're given a binary string representing a work schedule, where '1' is a workday and '0' is a rest day. You also have a base daily pay, a bonus earned whenever two consecutive days are both workdays, and a budget of k flips to turn rest days into workdays. What's the maximum total pay you can achieve, and what's the time and space complexity of your approach?

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I went straight to greedy and immediately realized that was wrong because flipping a single zero can affect two potential bonuses depending on its neighbors.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem: we need to choose up to k rest days to flip to workdays to maximize total pay = (number of workdays)*base + (number of adjacent workday pairs)*bonus. Then, recognize that flipping a rest day can create new adjacent pairs with existing workdays and with other flipped days, so we need to consider the marginal gain of each flip. A greedy approach using a max-heap of potential gains, or dynamic programming, can solve this efficiently; then analyze the time and space complexity.

Pro tip: Mention that the greedy approach works because the marginal gain of flipping a day is non-increasing as more flips are made, but be prepared to justify or switch to DP if the interviewer challenges it. Also, always clarify edge cases like k=0, all zeros, or all ones.

1. Clarify and formalize the problem

Restate the problem in your own words: given a binary string, base pay per workday, bonus for each adjacent pair of workdays, and up to k flips of '0' to '1', maximize total pay. Define the objective function clearly.

2. Identify the impact of flipping a day

Explain that flipping a '0' to '1' increases the workday count by 1 (adding base pay) and may create new adjacent workday pairs with neighboring '1's, each adding bonus. Also, flipping consecutive zeros can create additional pairs among the flipped days.

3. Choose an algorithm

Propose a greedy approach using a priority queue to always flip the rest day that yields the highest immediate gain, or a dynamic programming approach that considers the state of the previous day and the number of flips used. Discuss trade-offs.

4. Analyze time and space complexity

For greedy with heap: O(n log n) time and O(n) space. For DP: O(n*k) time and O(k) space (or O(n*k) if naive). Mention that DP can be optimized to O(n) space by keeping only the previous row.

5. Test with examples and edge cases

Walk through a small example, such as '1001' with k=1, to demonstrate the algorithm. Discuss edge cases: k=0, k >= number of zeros, all zeros, all ones, and large n.

Key Points to Mention

Objective function: total pay = base * (# of 1s) + bonus * (# of adjacent 1-1 pairs).
Flipping a '0' can create up to two new adjacent pairs (with left and right neighbors) and also pairs with other flipped days if consecutive zeros are flipped.
Greedy approach: use a max-heap to track the gain of flipping each '0', but note that gains can change after flips, so a simple greedy may not be optimal; DP is safer.
Dynamic programming state: dp[i][j][prev] = max pay for first i days, using j flips, with prev indicating if day i-1 was workday. Optimize space to O(k).
Time complexity: O(n*k) for DP, O(n log n) for greedy with heap; space complexity: O(k) for optimized DP, O(n) for greedy.
Edge cases: k=0, k >= number of zeros, string of all zeros, all ones, and very large n.

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