← Goldman Sachs Interview Insights
Went with a frequency map, two passes through the string.
Start by clarifying the problem: define 'first' (e.g., first in string order), what 'empty value' means (e.g., null, empty string, or a sentinel), and whether the string can contain any Unicode characters. Then propose an efficient solution using a hash map to count character frequencies in one pass, followed by a second pass to find the first character with count 1. Discuss time and space complexity, and consider edge cases like empty string or all repeating characters.
Pro tip: At Goldman Sachs, interviewers value clean, production-ready code and clear communication. Before coding, walk through a concrete example (e.g., 'stress') to validate your approach, and after coding, suggest optimizations like using an array for ASCII or a linked hash map for Unicode, showing you think about real-world constraints.
Ask about the definition of 'first' (order of appearance), the expected return for no unique character (null, empty string, etc.), and the character set (ASCII vs Unicode). Confirm input size and whether the string can be empty.
Propose a two-pass hash map solution: first pass to count frequencies, second pass to find the first character with count 1. Mention time O(n) and space O(k) where k is the number of distinct characters.
Write clean code with meaningful variable names. For example, in Java: use a HashMap<Character, Integer> or an int array if ASCII. Handle edge cases like empty string by returning null or empty string as agreed.
Walk through test cases: 'stress' returns 't', 'aabbcc' returns null, 'a' returns 'a', empty string returns null. Also test with special characters if relevant.
Mention alternative approaches: using an array for ASCII (O(1) space), or a linked hash map to combine counting and order. Discuss trade-offs between time and space, and potential follow-ups like streaming input.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use a hash map to accumulate total scores and counts per name, then compute averages and track the maximum. Clarify edge cases like empty input and negative scores, and discuss time/space complexity.
Pro tip: Mention that you would handle ties by returning any name with the highest average, and if needed, break ties alphabetically. Also, note that you would use a single pass for efficiency.
Ask about input format, empty list, negative scores, and tie-breaking. Confirm whether to return the name, the average, or both.
Use a hash map to store total score and count per name. This allows O(1) updates and easy average calculation.
Loop through the list, updating the total and count for each name. Keep track of the maximum average seen so far.
After processing all entries, compute the average for each name and compare to find the highest. Alternatively, compute on the fly if counts are known.
State time complexity O(n) and space O(k) where k is unique names. Walk through a small example to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.