Start by clarifying the problem and edge cases, then explain the dynamic programming approach where each row is built from the previous row. Emphasize the O(numRows^2) time and space complexity, and offer to code it up.
Pro tip: Mention that you can optimize space by only keeping the previous row, but since the output requires all rows, O(numRows^2) space is unavoidable. This shows you understand trade-offs.
Ask about constraints (e.g., numRows >= 0) and expected output format. Discuss handling numRows = 0 or 1.
Describe how each element (except first and last) is the sum of the two elements above it in the previous row. This forms the basis of the algorithm.
Initialize an empty list. For each row i from 0 to numRows-1, create a new row of length i+1, set first and last to 1, and fill middle using previous row.
State that time complexity is O(numRows^2) because we generate each element once. Space complexity is also O(numRows^2) for the output, but auxiliary space can be O(numRows) if only previous row is kept.
Write clean code with meaningful variable names. Test with small inputs like 0, 1, 2, 5 to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Started with a frequency map, no issues there.
Use a hash map to count the frequency of each element, then use a min-heap of size k or quickselect to find the k most frequent elements. Discuss trade-offs between approaches and handle edge cases like k larger than the number of unique elements.
Pro tip: Clarify whether the output order matters and if ties can be broken arbitrarily; also mention that for large datasets, a distributed approach like MapReduce could be used, showing awareness of scalability.
Ask about input size, whether k is guaranteed valid, if the array can be empty, and if the order of the k elements matters. This shows attention to detail.
Decide between sorting all unique elements by frequency (O(n log n)), using a min-heap of size k (O(n log k)), or quickselect (average O(n)). Explain your choice based on constraints.
Write clean code: first build a frequency map, then apply your chosen method to extract the top k. Use appropriate data structures and handle edge cases.
State time and space complexity clearly. For heap approach: O(n log k) time, O(n) space. For quickselect: average O(n) time, O(n) space.
Walk through a small example, including edge cases like k=1, k=number of unique elements, or all elements the same. Verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.