The setup clicked pretty fast once I mapped the index-as-difficulty constraint.
Clarify the problem first: the list's indices represent difficulty levels, so the list must be ordered by difficulty. Then propose generating it by iterating through difficulty levels from 0 to n-1, computing or retrieving the profit for each level, and appending to the list. Discuss how to handle missing levels (e.g., use 0 or None) and ensure the list length matches the maximum difficulty.
Pro tip: Mention that this representation enables O(1) lookup by difficulty, which is useful for dynamic programming or greedy algorithms. Also, ask if the list should be 0-indexed or 1-indexed, as that affects the mapping.
Confirm that index i corresponds to difficulty level i (or i+1 if 1-indexed). Ask about the range of difficulty levels and whether all levels are present.
Identify where the profit values come from: a function, a database, or a given array of (difficulty, profit) pairs. If from pairs, sort by difficulty and fill gaps.
Decide on a default value for missing difficulties (e.g., 0, -1, or None) and ensure the list length covers the maximum difficulty.
Initialize a list of size max_difficulty+1 with default values, then populate it by iterating over the data source and assigning profit to the correct index.
Check that the list is correctly ordered and complete. Mention time and space complexity (O(n) or O(max_difficulty)) and potential optimizations.
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