Start by clarifying the problem constraints (e.g., sorted ascending, duplicates allowed, in-place vs new array) and then propose the two-pointer technique to merge in O(n+m) time. Walk through the algorithm step-by-step, discuss edge cases, and analyze time/space complexity.
Pro tip: At Apple, interviewers value clean, efficient code and attention to edge cases. After presenting the two-pointer solution, mention that if one array is much larger, a binary search approach can reduce comparisons, showing you consider scalability.
Ask about input sizes, sorted order, duplicates, and whether the merge should be in-place or produce a new array. Confirm the expected output format.
Explain that you'll use two pointers starting at the beginning of each array, compare elements, and append the smaller one to the result while advancing that pointer.
Trace the algorithm on a small example (e.g., [1,3,5] and [2,4,6]) to demonstrate correctness and handle remaining elements after one array is exhausted.
State time complexity O(n+m) and space complexity O(n+m) for a new array (or O(1) extra if merging in-place from the end). Discuss edge cases like empty arrays, one array much larger, and duplicates.
Implement the solution in your preferred language with meaningful variable names, then mentally test with edge cases. If time permits, mention alternative approaches like binary search for skewed sizes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Track the minimum price seen so far as you scan left to right, and at each step compute what profit you'd get selling today.
Clarify the problem constraints (e.g., array size, price range) and edge cases, then propose a one-pass O(n) solution that tracks the minimum price seen so far and computes the maximum profit at each step. Walk through a small example to validate the logic before coding.
Pro tip: Emphasize that the optimal solution is O(n) time and O(1) space, and mention that a brute-force O(n^2) approach would be unacceptable for large inputs—this shows you think about scalability and efficiency, which Apple values.
Ask clarifying questions about input size, price range, and whether multiple transactions are allowed. Confirm that only one buy-sell is permitted and that the buy must occur before the sell.
Briefly mention the O(n^2) brute-force approach, then explain why it's inefficient. Introduce the O(n) one-pass solution that tracks the minimum price and maximum profit.
Use a small array (e.g., [7,1,5,3,6,4]) to demonstrate how the algorithm updates min_price and max_profit step by step, ensuring the interviewer follows your reasoning.
Write clean, well-commented code implementing the one-pass algorithm. Handle edge cases like empty array or decreasing prices by returning 0.
State the time and space complexity (O(n) time, O(1) space). Discuss potential edge cases (e.g., all prices equal, single element) and verify the solution with them.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use a stack to track opening brackets. Iterate through the string: push opening brackets, and for closing brackets, check if the stack top matches the corresponding opener; if not, return false. At the end, the stack must be empty.
Pro tip: Clarify the bracket types and edge cases (empty string, single type) upfront, and mention that this approach runs in O(n) time and O(n) space, which is optimal for this problem.
Ask if the string can contain other characters, if it can be empty, and which bracket types to support (e.g., (), [], {}). Confirm that an empty string is considered valid.
Explain that a stack is ideal because brackets must be closed in LIFO order. Mention that a hash map can map closing brackets to opening brackets for quick lookup.
Iterate through each character: if it's an opening bracket, push onto the stack; if it's a closing bracket, check if the stack is non-empty and the top matches the corresponding opener. If not, return false. After the loop, return true only if the stack is empty.
State that time complexity is O(n) and space complexity is O(n) in the worst case. Walk through examples like '()[]{}', '([)]', and '' to demonstrate correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.