Pretty standard sliding window with a queue.
Start by clarifying the problem: moving average of a data stream with a fixed window size. Then design a class that maintains a queue of the last N elements and a running sum, updating the average in O(1) time per element. Discuss edge cases like window not full and integer overflow.
Pro tip: Mention that you can optimize memory by using a circular buffer instead of a queue, and that the running sum approach avoids O(N) recomputation. Also, discuss thread-safety if the stream is concurrent.
Ask about window size, data types (int/double), and whether the stream is continuous. Confirm expected time complexity for each operation.
Choose a queue (or circular buffer) to store the window elements and a variable to keep the running sum. This allows O(1) updates.
Write a constructor to initialize window size, and a next(val) method that adds the new value, removes the oldest if window is full, updates sum, and returns the average.
Consider when the window is not yet full, division by zero, and potential integer overflow. Use double for sum and average.
State that each next() call is O(1) time and O(window size) space. Discuss trade-offs with alternative approaches like recomputing sum.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one tripped me up more than I expected.
Clarify that a shift means a cyclic rotation, then use a canonical representation such as the lexicographically smallest rotation or a doubled-string substring to group equivalent strings. Hash each canonical form to a list of original strings, and discuss time/space complexity and edge cases.
Pro tip: Mention that you can compute the canonical form in O(n) using Booth's algorithm, but for interviews, the doubled-string approach with substring search is often sufficient and easier to explain. Also, handle empty strings and duplicates gracefully.
Confirm that 'shift' means cyclic rotation (e.g., 'abc' shifts to 'bca', 'cab') and that grouping should include all strings that are rotations of each other. Ask about input size, character set, and whether duplicates should be preserved.
For each string, compute a canonical key that is identical for all its rotations. Options: lexicographically smallest rotation (using Booth's algorithm in O(n)), or the smallest substring of length n in s+s (using KMP or similar).
Iterate through the list, compute the canonical key for each string, and append the original string to a list in a hash map keyed by the canonical form. This groups all rotations together.
Discuss time complexity: O(N * L) where N is number of strings and L is average length, assuming O(L) canonicalization. Space: O(N * L) for the map. Handle edge cases: empty strings, single-character strings, strings with repeated characters, and duplicate strings.
Walk through a small example to verify correctness. If needed, optimize canonicalization (e.g., Booth's algorithm) or consider alternative approaches like sorting each string's rotations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Felt straightforward but there are a bunch of edge cases around the boundaries that'll get you if you're not careful.
Use a single pass through the sorted array, tracking the next expected number in the range. When a gap is found between the current expected number and the current array element, add the missing range, then update the expected number to the current element + 1. After the loop, check for any remaining gap up to the upper bound.
Pro tip: Clarify edge cases upfront: whether the range is inclusive, how to handle duplicates, and what to return if no missing ranges exist. This shows attention to detail and prevents incorrect assumptions.
Confirm the range bounds (inclusive/exclusive), input constraints (sorted, possible duplicates), and expected output format (e.g., list of strings like 'a->b' or 'a').
Set a variable `next` to the lower bound of the range. This represents the next number that should be present in the array.
For each number `num` in the array, if `num > next`, add the missing range from `next` to `num-1`. Then update `next` to `num + 1` (skip duplicates by only updating if `num >= next`).
After the loop, if `next <= upper`, add the missing range from `next` to `upper`.
Convert each missing range to the required string format (e.g., 'a->b' if a != b, else 'a') and return the list.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the problem constraints (e.g., character set, case sensitivity, in-place vs. new string) and then propose an efficient solution using a hash set to track seen characters while building the result. Walk through the algorithm with a simple example, analyze time and space complexity, and discuss potential optimizations or trade-offs.
Pro tip: Mention that for ASCII strings, a boolean array of size 128 can replace the hash set for O(1) lookups with lower constant factors, showing awareness of practical performance. Also, discuss how to handle Unicode characters if the interviewer raises that possibility.
Ask about the character set (ASCII vs. Unicode), case sensitivity, whether the input can be modified in-place, and expected output format. This ensures you solve the correct problem.
Select a hash set to track seen characters for O(1) average lookup, or a boolean array for ASCII to optimize space and speed. Consider if additional data structures like a StringBuilder are needed for efficient string construction.
Iterate through the string, and for each character, check if it has been seen. If not, append it to the result and mark it as seen. This preserves the order of first appearances.
State time complexity O(n) and space complexity O(k) where k is the number of unique characters. Discuss edge cases: empty string, all duplicates, no duplicates, and Unicode characters.
Walk through a test case to verify correctness. If needed, discuss in-place modification for character arrays to save space, or using bit manipulation for lowercase letters.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the definition of stability and the constraints (e.g., input size, data types, memory limits) before proposing a solution. Then discuss the trade-offs between comparison-based sorts (e.g., merge sort for stability) and non-comparison sorts (e.g., counting sort for integers), and justify your choice based on time/space complexity. Finally, outline the algorithm and analyze its complexity.
Pro tip: Mention that in practice, many standard library sorts (like Python's Timsort or Java's TimSort) are stable and optimized, so you'd likely use them unless there's a specific reason to implement your own. This shows awareness of real-world engineering.
Ask about input size, data types, memory limits, and what 'stability' means in this context (e.g., preserving relative order of equal elements). Confirm if the array can be modified in-place.
Compare stable sorting algorithms (merge sort, insertion sort, bubble sort) and non-comparison sorts (counting sort, radix sort) if applicable. Consider built-in stable sorts.
For each candidate, state average/worst-case time and space complexity. Highlight that merge sort is O(n log n) time and O(n) space, while in-place sorts like insertion sort are O(n^2) time but O(1) space.
Choose the algorithm that best balances stability, time, and space based on constraints. For general case, merge sort or Timsort is often optimal; for small arrays, insertion sort may be efficient.
Describe the algorithm steps, handle edge cases (empty array, single element, duplicates), and mention potential optimizations (e.g., using insertion sort for small subarrays in merge sort).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.