Greedy felt obvious to me once I read it twice.
First, clarify the problem: are the overhead costs independent per position, or are there constraints between adjacent characters? If independent, the solution is to greedily choose the minimum cost character for each '?'. If there are constraints (e.g., no two adjacent same characters), use dynamic programming to minimize the total cost while satisfying constraints.
Pro tip: Always ask clarifying questions before diving into a solution. For Amazon, demonstrate customer obsession by ensuring you fully understand the requirements and constraints before proposing an approach.
Ask about the cost structure: Is the cost per position independent? Are there any constraints on the final string (e.g., no adjacent duplicates)? What is the range of characters? This determines the algorithm.
State that the goal is to minimize the sum of overhead costs for all replaced '?' positions, possibly subject to constraints.
If independent, use a greedy approach: for each '?', pick the character with the lowest cost. If constrained, use dynamic programming with state representing the previous character.
For greedy: O(n * k) where k is alphabet size. For DP: O(n * k^2) or O(n * k) with optimization. Mention space complexity.
Consider empty string, all '?', no '?', large input, and constraints like no adjacent duplicates. Walk through a small example to verify.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.