Backtracking felt straightforward and I got through it fine.
Start by clarifying the problem constraints and edge cases, then present a recursive backtracking solution that builds combinations digit by digit. After coding, analyze the time and space complexity, focusing on the exponential growth for 9-digit inputs, and discuss strategies for streaming results to handle memory constraints.
Pro tip: Mention that for a 9-digit input, the output size can be up to 4^9 = 262,144 combinations (if all digits are 7 or 9), which is manageable in memory, but for longer inputs or repeated calls, streaming or lazy generation becomes essential. Also, highlight that Uber often deals with large-scale data, so showing awareness of memory and I/O efficiency is crucial.
Ask about input constraints (e.g., digits 2-9 only, empty string, length limits) and expected output format (list, generator, etc.). Confirm whether the solution should handle invalid characters.
Explain a recursive backtracking approach: map each digit to its letters, then recursively build combinations by appending each letter for the current digit and moving to the next. Alternatively, use iterative BFS with a queue.
Derive time complexity O(4^n * n) where n is the number of digits (since each digit maps to 3 or 4 letters), and space complexity O(n) for recursion depth plus output size. For n=9, output size is at most 4^9 = 262,144, which is feasible in memory.
If the output is large or the function is called repeatedly, propose using a generator (yield) to produce combinations lazily, or process results in chunks. Mention that for very large n, streaming avoids storing all combinations in memory.
Relate to ML pipelines: generating combinations might be part of feature engineering or data augmentation. Emphasize efficiency, parallelization, and integration with data loaders.
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