Got through it fine and had something close to the optimal approach ready for the follow-up too.
Start by clarifying the problem and edge cases, then propose a solution using a min-heap to track end times of ongoing meetings. Sort intervals by start time, and for each meeting, if the earliest ending meeting has ended, reuse that room; otherwise allocate a new room. The heap size at the end gives the minimum number of rooms.
Pro tip: Mention that this problem is equivalent to finding the maximum number of overlapping intervals, which can also be solved by sorting start and end times separately and using a two-pointer sweep. This shows deeper insight and awareness of alternative approaches.
Confirm the input format, whether intervals are inclusive/exclusive, and if meetings can be split. Restate the goal: minimize rooms so no two meetings overlap in the same room.
Mention a naive O(n^2) approach checking overlaps, then explain how sorting and a heap reduce it to O(n log n).
Sort intervals by start time. Use a min-heap of end times. For each interval, if the heap's minimum end time <= current start, pop it (reuse room). Push current end time. The heap size is the answer.
State time complexity O(n log n) due to sorting and heap operations, space O(n). Discuss edge cases: empty list, single meeting, all overlapping, none overlapping.
Walk through a small example like [[0,30],[5,10],[15,20]] to demonstrate correctness and verify the algorithm step by step.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one was a mess, and not entirely my fault but also kind of my fault.
Clarify the tree structure and operator set, then propose a recursive post-order traversal that evaluates children before applying the operator. Discuss handling of edge cases like division by zero, unary operators, and integer vs floating-point results.
Pro tip: Mention that you can evaluate the expression tree in O(n) time and O(h) space, and that an iterative post-order traversal avoids recursion depth issues for very deep trees.
Ask about the node structure (value, left, right), supported operators (+, -, *, /), and expected output type (integer or float). Confirm whether the tree is guaranteed valid and if division by zero can occur.
Explain that a post-order traversal (left, right, root) is natural because operands must be evaluated before the operator. You can implement it recursively or iteratively using a stack.
Define a function evaluate(node) that returns the numeric value. If the node is a leaf (operand), return its value. Otherwise, recursively evaluate left and right children, then apply the operator to the results.
Discuss division by zero (throw an error or return infinity), unary minus, and integer overflow. Also consider whether the tree can be empty or contain invalid operators.
State that time complexity is O(n) and space complexity is O(h) for recursion (or O(n) for iterative stack). Mention that iterative traversal avoids stack overflow for deep trees.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem constraints and edge cases, then discuss a brute-force solution before optimizing with appropriate data structures or algorithms. Think aloud to demonstrate your problem-solving process, and analyze time and space complexity for each approach.
Pro tip: At Google, interviewers value clean, bug-free code and strong communication over rushing to an optimal solution. Write modular code with meaningful variable names and test with edge cases before declaring completion.
Ask clarifying questions about input size, constraints, edge cases, and expected output. Restate the problem in your own words to confirm understanding.
Walk through a few examples, including edge cases, to solidify your understanding and identify patterns or potential pitfalls.
Propose a brute-force solution first, then discuss optimizations using appropriate data structures or algorithmic techniques. Compare trade-offs.
Write clean, modular code while explaining your logic. Test with the examples and edge cases, debugging as needed.
State the time and space complexity of your final solution. Discuss potential improvements or alternative approaches if time permits.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use the STAR method to tell a concise story about a time you navigated ambiguity, focusing on your actions and learnings. Emphasize how you proactively sought clarity, adapted to changes, and delivered results while aligning with Google's values like user focus and collaboration.
Pro tip: Show self-awareness by acknowledging what you didn't know and how you learned from the experience, rather than claiming you had all the answers. This demonstrates humility and a growth mindset, which Google values highly.
Briefly describe the situation and why it was ambiguous, including any constraints or stakeholder expectations. Keep it concise to leave time for your actions.
Explain the specific problem or uncertainty you faced and why it mattered. This shows you can identify key issues in ambiguous situations.
Describe the steps you took to bring clarity, such as asking questions, researching, prototyping, or collaborating. Focus on your thought process and how you adapted as new information emerged.
Quantify the results if possible, and explain how your actions led to a successful resolution. Mention any positive feedback or impact on the team or product.
Conclude with what you learned and how you've applied that learning to future ambiguous situations. This shows growth and adaptability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.