Binary search on the answer is the move here but my brain just went blank.
Recognize this as a classic 'minimize the maximum' problem that can be solved efficiently using binary search on the answer combined with a greedy feasibility check. Explain that the search space is between the maximum element (lower bound) and the total sum (upper bound), and for each candidate maximum sum, check if the array can be split into at most k subarrays without exceeding that sum.
Pro tip: Emphasize that the greedy check is optimal for a fixed maximum sum because taking the longest possible subarray without exceeding the limit minimizes the number of subarrays needed. Also, mention that if the number of subarrays is less than k, you can always split further without increasing the maximum sum, so the condition is 'at most k'.
Restate the problem to ensure understanding: split array into exactly k non-empty contiguous subarrays, minimize the largest sum. Ask about edge cases like k=1, k=n, or when elements are zero.
Recognize this as a binary search on the answer problem. The monotonic property: if a maximum sum S is feasible, any larger sum is also feasible.
Write a greedy function that, given a candidate maximum sum, iterates through the array and counts how many subarrays are needed. If the count exceeds k, the candidate is infeasible.
Set low = max(array) and high = sum(array). While low < high, compute mid, check feasibility, and adjust bounds accordingly. Return low as the minimized largest sum.
State time complexity O(n log(sum - max)) and space O(1). Walk through a small example to verify correctness and discuss potential off-by-one errors.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Didn't even get to think about this one clearly.
First, clarify the game rules and win conditions, as the problem statement is ambiguous. Then, outline a systematic algorithm to determine the winner, focusing on edge cases and testability. Finally, discuss how you would test the solution to ensure correctness.
Pro tip: Demonstrate a QA mindset by proactively identifying ambiguities and proposing test cases before diving into the algorithm. This shows you think like a test engineer, not just a developer.
Ask questions to understand the game: What are the winning conditions? How are hands compared? Are there ties? This ensures you solve the correct problem.
Represent each card as a pair of letters (e.g., a tuple or string). Represent a hand as a list of such cards.
Outline steps to evaluate each hand according to the rules, compare hands, and determine the winner. Consider sorting, counting, or pattern matching as needed.
List potential edge cases: empty hands, duplicate cards, invalid inputs, ties, and maximum hand sizes. Explain how your algorithm handles them.
Propose specific test cases to validate the solution, including normal cases, edge cases, and performance tests if applicable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is a classic topological sort problem and I actually knew that going in.
Model the problem as a directed graph where each letter is a node and edges represent the relative order derived from adjacent word pairs. Then perform a topological sort to produce a valid alphabet order, while handling edge cases like invalid input or cycles.
Pro tip: As a QA engineer, emphasize test cases for edge conditions such as cycles, conflicting orders, and prefix violations, and mention that you would validate the derived order against the original list.
Iterate through adjacent word pairs and compare characters to find the first differing character, adding a directed edge from the first to the second. If one word is a prefix of the other and the longer word comes first, the input is invalid.
Initialize in-degree counts for all unique characters and increment the in-degree of the destination node for each edge.
Use Kahn's algorithm (BFS with a queue) or DFS to produce a linear ordering of characters. If the result does not include all unique characters, a cycle exists and no valid order is possible.
Check that the derived order is consistent with the original list by verifying that each adjacent pair is correctly ordered. Return the order as a string.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.