"Suppose you have a list of programming languages and research tools collected from project notes, but the same tool may appear with different capitalization or word order. How would you normalize and group equivalent names, and what data structures would you use?"
Before grouping values, convert each string into a canonical form. Common steps include lowercasing, trimming whitespace, removing punctuation, and standardizing separators so small formatting differences do not split identical tools into separate groups.
name = ''.join(ch.lower() for ch in raw if ch.isalnum() or ch.isspace()).split()
A hash table is the natural structure for collecting all raw variants under one normalized key. It gives average O(1) insertion and lookup, which makes it efficient for scanning the list once.
groups.setdefault(key, []).append(raw_name)
If word order should not matter, sorting tokens creates a stable signature. For example, two strings with the same words in different orders can map to the same sorted-token key.
key = ' '.join(sorted(tokens))
Aggressive normalization can merge truly different tools, while weak normalization can miss duplicates. In interviews, it is important to state what equivalence rule you assume and how that choice affects correctness.