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Can't even remember what it was, which probably tells you how trivial it felt.
Since the specific details are not recalled, treat this as a general algorithmic warm-up. Start by restating the problem in your own words to confirm understanding, then discuss a straightforward solution before optimizing. Walk through a small example to validate your logic and analyze time/space complexity.
Pro tip: Even for an easy question, explicitly state your assumptions and edge cases (e.g., empty input, single element) before coding. This shows thoroughness and often reveals hidden requirements that interviewers care about.
Ask clarifying questions to pin down input format, output format, constraints, and edge cases. Restate the problem to ensure alignment with the interviewer.
Briefly outline the simplest solution, even if inefficient, to establish a baseline. Mention its time and space complexity.
Identify bottlenecks and propose improvements using appropriate data structures or algorithms. Explain the trade-offs and the new complexity.
Trace your algorithm on a small input, including edge cases, to verify correctness and catch off-by-one errors.
Write clean, modular code with meaningful variable names. After coding, mentally run through test cases and discuss potential bugs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the problem constraints and edge cases, then outline a solution that iterates through each word, computes the absolute difference between consonant and vowel counts, and sorts using a custom comparator that breaks ties alphabetically. Discuss time and space complexity, and consider potential optimizations or alternative approaches.
Pro tip: Mention that you would confirm whether 'y' is considered a vowel and how to handle empty strings or punctuation, showing attention to detail and robustness. Also, note that sorting stability isn't required because ties are broken alphabetically, so any efficient sort works.
Ask about input format, definition of vowels (a, e, i, o, u), handling of non-letter characters, and whether the sentence can be empty. Confirm output format (list of words).
For each word, count vowels and consonants, compute absolute difference. Store words with their difference. Sort the list by difference ascending, then alphabetically for ties.
Write code using a custom comparator or key function. Test with examples, including ties and edge cases like single-word sentences or words with equal vowels and consonants.
Time complexity: O(n * m + n log n) where n is number of words and m is average word length. Space complexity: O(n) for storing words and differences.
Mention that counting can be done in one pass per word. If many words, sorting dominates. Could use counting sort if differences are bounded, but not necessary.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
BFS simulation, just like the classic problem.
Model the problem as a multi-source BFS where all initially infected plants are enqueued at day 0, and each BFS layer represents one day of spread. Process exactly X layers, then return the grid; if the queue empties before X days, the remaining healthy plants are safe.
Pro tip: Clarify edge cases upfront—X = 0, no infected plants, or X larger than needed—and mention that you can early-terminate once the queue is empty to avoid unnecessary work.
Confirm what counts as a neighbor (4-directional), whether infection spreads simultaneously each day, and the grid dimensions and value ranges. Ask about edge cases like X = 0 or no infected plants.
Use multi-source BFS with a queue initialized with all infected cells, and track the number of days processed. Explain why BFS is optimal for simultaneous, level-by-level spread.
For each day up to X, process the current queue size as one layer, infecting healthy neighbors and enqueueing them. Stop early if the queue becomes empty.
After X days or early termination, return the modified grid. Optionally, discuss how to detect if any healthy plants remain uninfected.
State time complexity O(rows * cols) and space O(rows * cols) for the queue. Walk through a small example and edge cases to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the problem constraints (number of operations, coordinate range, memory limits) and discuss trade-offs between data structures like balanced BSTs, sorted arrays, and hash sets. Then propose an efficient solution, such as using a balanced BST to store obstacles and performing range queries, and analyze time and space complexity.
Pro tip: Mention that if the coordinate range is bounded and small, a bitset or Fenwick tree can provide O(1) or O(log M) updates and queries with low constant factors, which is often preferred in practice.
Ask about the number of operations, coordinate bounds, whether obstacles can be removed, and if queries are online or offline. This determines the optimal data structure.
Select a data structure that supports fast insertion and range emptiness queries, such as a balanced BST (e.g., TreeSet in Java) or a sorted list with binary search. Discuss alternatives like hash sets (which don't support range queries efficiently) and bitsets (if coordinates are bounded).
For insertion, add the obstacle to the data structure. For query, compute the range [L-(N-1), L+(N-1)] and check if any obstacle falls within it, e.g., by finding the smallest obstacle >= lower bound and checking if it's <= upper bound.
State time complexity: O(log K) per operation for balanced BST, where K is number of obstacles. Space O(K). Compare with alternatives: bitset O(1) but O(M) space; sorted array O(K) insertion but O(log K) query. Choose based on constraints.
Consider empty obstacle set, duplicate placements, large N covering entire range, and coordinate overflow. Write pseudocode and test with examples.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.