I'd done scheduling problems before so the core logic wasn't the scary part.
Clarify the problem constraints (e.g., whether meetings are sorted, if working hours are given) and then propose an efficient algorithm: sort meetings by start time, merge overlapping intervals, and scan for the first gap that can accommodate the new meeting's duration. Discuss trade-offs between sorting and using a heap for streaming data, and handle edge cases like no available slot.
Pro tip: Mention that you would confirm whether the input is sorted or if you can sort it, and discuss the possibility of using a min-heap for a streaming scenario to show adaptability to real-world constraints.
Ask about input format, whether meetings are sorted, if working hours or earliest start are given, and if the new meeting must fit within a single day. Confirm edge cases like no available slot.
Decide between sorting the intervals (O(n log n)) or using a heap for streaming (O(n log n) with O(n) space). Consider if intervals can be merged first to simplify gap finding.
Sort meetings by start time, merge overlapping intervals, then iterate through gaps (including before first and after last) to find the earliest start where the new meeting fits. Apply working hours or earliest start constraints.
State time and space complexity, and discuss trade-offs: sorting is simple but requires all data; heap is better for streaming but more complex. Mention if binary search could be used on sorted intervals.
Consider empty list, meetings that overlap, no available slot, and constraints like working hours. Walk through a small example to verify correctness.
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Said O(n log n) for the sort and O(n) for the scan, so O(n log n) total.
Start by clearly stating the time complexity using Big-O notation, then briefly explain how you derived it by analyzing the loops, recursion, or operations in your code. Also mention space complexity if relevant, and discuss any trade-offs you made between time and space.
Pro tip: Always relate the complexity to the input size and mention best, average, and worst cases if they differ. If you optimized from a brute-force solution, explain the improvement to show your problem-solving process.
Clearly state the time complexity in Big-O notation, e.g., O(n log n). If there are multiple parts, specify the overall complexity.
Walk through the code or algorithm, identifying the dominant operations and how they scale with input size. For example, nested loops multiply, sequential loops add.
If relevant, state the space complexity and explain how extra data structures or recursion contribute to it.
If you made a trade-off (e.g., using extra space to reduce time), explain why it was beneficial and any alternatives considered.
Mention if the complexity changes for best, average, or worst-case scenarios, and how it handles edge cases like empty input.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I actually felt pretty good.
Start by clarifying the problem requirements and constraints, then systematically outline test cases covering normal, edge, and error scenarios. Emphasize the importance of testing for correctness, performance, and scalability, especially for a large-scale system like LinkedIn.
Pro tip: Mention that you prioritize test cases based on risk and impact, and discuss how you would automate and integrate them into a CI/CD pipeline to ensure continuous quality.
Ask questions to understand the problem's inputs, outputs, constraints, and expected behavior. Confirm assumptions about data types, ranges, and performance expectations.
Outline categories such as functional, boundary, edge, error handling, performance, and security tests. Consider both positive and negative scenarios.
For each category, list concrete test cases with input values and expected outcomes. Include normal cases, boundary values, invalid inputs, and stress conditions.
Explain which test cases are most critical and why, based on likelihood of failure and impact. Discuss trade-offs if time is limited.
Describe how you would automate these tests and integrate them into a CI/CD pipeline. Mention tools and frameworks relevant to the tech stack.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.