← Anthropic Interview Insights
Clarify the dataset's characteristics (size, data types, memory constraints) and whether the data is sorted or streamed. Then propose an efficient algorithm: sort the data for median and mode, or use a hash map for mode and quickselect for median. Discuss trade-offs between time and space complexity, and handle edge cases like multiple modes or even-sized datasets.
Pro tip: Mention that for large or streaming data, exact median and mode may require approximation algorithms (e.g., t-digest for median, count-min sketch for mode), showing awareness of real-world constraints. Also, explicitly state how you'd handle ties for mode (e.g., return all modes or the smallest).
Ask about dataset size, data type, memory limits, whether the data is static or streaming, and if multiple modes are allowed. This determines the algorithm choice.
For median: sort and pick middle, or use quickselect for O(n) average. For mode: use a hash map to count frequencies, then find max. Discuss trade-offs.
Address even-sized datasets (average two middle values), multiple modes (return all or specify rule), empty dataset, and single-element dataset.
State time and space complexity for each approach. For large data, consider sorting once for both (O(n log n)) or using separate O(n) methods.
If data is huge or streaming, mention approximate algorithms (e.g., t-digest, count-min sketch) and their trade-offs in accuracy vs. resource usage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.