Start by clarifying requirements and constraints, then outline the core design using an array of buckets with separate chaining for collision resolution. Walk through the implementation of put, get, and remove, explaining how resizing works when the load factor exceeds a threshold. Finally, discuss trade-offs and potential optimizations.
Pro tip: Mention that you would use a prime number for the bucket array size to reduce clustering, and that resizing should double the capacity and rehash all existing entries. Also, proactively discuss handling of null keys/values and thread-safety if relevant.
Ask about expected key/value types, load factor threshold, and whether thread-safety is needed. Confirm that no built-in collections (like arrays or lists) are allowed, but basic arrays are permitted.
Propose an array of buckets, where each bucket is a linked list (or a custom dynamic array) of key-value pairs. Explain that collisions are handled via separate chaining.
Describe put: compute hash, find bucket, traverse chain to update or append. Describe get: compute hash, traverse chain to find key. Describe remove: compute hash, traverse chain to find and remove node, adjusting links.
Track the number of entries and the load factor (entries / bucket count). When load factor exceeds a threshold (e.g., 0.75), create a new array of double the size (preferably a prime), rehash all existing entries, and replace the old array.
Talk about time complexity (average O(1), worst O(n)), choice of hash function, and potential improvements like using balanced trees for long chains. Mention handling of null keys/values and thread-safety if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Sort first by start time, then walk through and merge.
Start by clarifying the problem constraints (e.g., interval inclusivity, input size) and then propose sorting the intervals by start time. After sorting, iterate through the intervals and merge overlapping ones by comparing the current interval's start with the previous merged interval's end. This yields O(n log n) time due to sorting and O(n) space for the output.
Pro tip: Mention edge cases like empty input, single interval, and intervals that are adjacent but not overlapping (e.g., [1,2] and [2,3]) to show thoroughness. Also, discuss how you would handle large inputs that don't fit in memory, demonstrating scalability awareness.
Ask about interval inclusivity (closed intervals), input size, and whether the output should be sorted. Confirm that intervals are given as pairs of integers.
Sort the intervals by their start times. This is crucial because it allows linear merging by ensuring that any overlapping intervals are adjacent.
Initialize an empty result list. Iterate through sorted intervals; if the result is empty or the current interval's start is greater than the last merged interval's end, add it to the result. Otherwise, merge by updating the last interval's end to the maximum of both ends.
After processing all intervals, return the result list, which contains non-overlapping intervals sorted by start time.
State that time complexity is O(n log n) due to sorting, and space complexity is O(n) for the output. Discuss edge cases like empty input, single interval, and intervals that touch but don't overlap.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use a two-pointer technique to traverse both sorted lists simultaneously, comparing the current intervals to find intersections. At each step, advance the pointer of the interval that ends first, as it cannot intersect with any subsequent interval in the other list. Collect all intersections and return them.
Pro tip: Clarify upfront whether the output should be sorted and whether intervals are closed or open, as this affects edge cases. Also, mention that the algorithm runs in O(m+n) time and O(1) extra space (excluding output), which is optimal for this problem.
Confirm that each list is sorted and pairwise-disjoint, and that intervals are typically closed. Ask about output format and edge cases (e.g., touching endpoints).
Set two pointers, one for each list, starting at index 0. Create an empty list to store the intersections.
Compute the intersection of the current intervals: start = max(start1, start2), end = min(end1, end2). If start <= end, add [start, end] to the result.
Move the pointer of the interval that ends first, because it cannot overlap with any later interval in the other list. If ends are equal, advance both.
After the loop, return the list of intersections. State that time complexity is O(m+n) and space is O(1) extra (or O(k) for output).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Two pointers from both ends, move the shorter side inward.
Start by clarifying the problem and edge cases, then propose a brute-force solution to establish baseline understanding. Optimize using a two-pointer technique that starts with the widest container and moves the pointer pointing to the shorter line inward, explaining why this is safe. Analyze time and space complexity, and discuss potential variations or optimizations.
Pro tip: At Amazon, interviewers value candidates who not only solve the problem but also articulate trade-offs and consider scalability. Explicitly state why the two-pointer approach is optimal and mention that it runs in O(n) time with O(1) space, which is crucial for large inputs.
Ask clarifying questions about input constraints (e.g., array size, height values) and confirm the goal: maximize area = min(height[i], height[j]) * (j - i). Restate the problem in your own words to ensure alignment.
Describe a naive O(n^2) solution that checks all pairs of lines. This shows you can think of a straightforward approach and sets the stage for optimization.
Explain the two-pointer technique: initialize left at 0 and right at n-1, compute area, then move the pointer pointing to the shorter line inward. Justify why moving the shorter line cannot miss a larger area.
State that the two-pointer approach runs in O(n) time and O(1) space, which is optimal. Compare with brute force to highlight efficiency gains.
Walk through a small example (e.g., [1,8,6,2,5,4,8,3,7]) to demonstrate correctness. Mention edge cases like empty array, two elements, or all equal heights.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Harder than it looks and I blanked for a second on the difference between this and the previous problem.
Start by clarifying the problem and walking through a small example to confirm understanding. Then present a two-pointer solution that computes trapped water in O(n) time and O(1) space, explaining the invariant that water above each bar is min(max_left, max_right) - height. Finally, discuss trade-offs with alternative approaches like dynamic programming or stack-based solutions.
Pro tip: Mention that you would validate the solution with edge cases like empty array, single bar, strictly increasing/decreasing heights, and flat terrain. This shows attention to detail and production-quality thinking, which Amazon values.
Restate the problem in your own words and ask clarifying questions about input constraints (e.g., array size, height range) and expected output. Walk through a small example to ensure alignment.
Briefly describe a naive O(n^2) approach: for each bar, compute max height to its left and right, then sum min(left_max, right_max) - height. This establishes a correct baseline and shows you can start simple.
Explain the O(n) time, O(1) space two-pointer technique: maintain left and right pointers, track max_left and max_right, and move the pointer with the smaller max inward, adding trapped water at each step.
State time and space complexity of the two-pointer solution and compare with alternatives like dynamic programming (O(n) space) or stack-based (O(n) space). Discuss when each might be preferable.
Walk through edge cases (empty array, one element, increasing/decreasing, flat) and verify the algorithm produces correct results. Mention potential overflow or integer issues if heights are large.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.