# Nested List Weighted Sum & Car Rental Interval Scheduling #### Watch someone solve the nested list weighted sum & car rental interval scheduling problem in an interview with a FAANG engineer and see the feedback their interviewer left them. Explore this problem and others in our library of interview replays. ### Interview Summary **Problem type** Nested List Weighted Sum & Car Rental Interval Scheduling **Interview question** - **Problem 1:** Given a nested list of integers where each element is either a number or another nested list, compute a weighted sum where each number is multiplied by its depth level. The solution requires recursive traversal and careful level tracking. - **Problem 2:** Given a list of car rental requests with start and end times, determine the minimum number of cars needed to satisfy all requests simultaneously. The optimal solution involves sorting by start time and using a min-heap to efficiently track active rentals — improving from a brute-force O(n²) approach to O(n log n). ### Interview Feedback **Feedback about Wily Tornado (the interviewee)** - Advance this person to the next round? **Yes** - How were their technical skills? **2/4** - How was their problem solving ability? **3/4** - What about their communication ability? **4/4** > **STRENGTHS AND WHAT WENT WELL** > - Communication and Presence: You have a fantastic, friendly, and humorous interviewing presence. You were incredibly open about your comfort areas from the start, making the session highly collaborative. > - Core Algorithmic Intuition: Your instinct for matching problems to patterns is strong. You immediately recognized that the Depth Sum problem required a recursive approach and implemented it flawlessly. > - Dry-Running and Execution: Once you had the recursion down for the first problem, you performed a very clean dry-run. Testing your code against the example and tracing the execution to produce the correct result was a high point. > - Pattern Recognition: On the Rental Car problem, even when you got stuck on the initial approach, you were able to identify that a heap could be used to optimize the solution. > **AREAS FOR IMPROVEMENT** > - Clarifying Questions and Edge Cases: For both problems, you jumped straight into the solution without asking clarifying questions or defining edge cases (e.g., handling empty inputs, negative values, or overlapping time boundaries). Get into the habit of pausing to define the boundaries of a problem before writing code. > - Initial Brute-Force Scaling: On the Rental Car problem, your initial instinct was to iterate hour-by-hour. When challenged on how that would scale if the data included minutes or spanned months, you needed a hint to pivot. Try to think about data scale and input types early on. > - Conditional Logic and Complexity: You mentioned struggling with complex conditional logic, which showed up a bit when structuring the active car tracking. Additionally, calculating and optimizing time complexities (runtime) on the fly is an area that needs tighter execution to avoid falling back on high-complexity solutions like O(N²). > **ADVICE FOR FUTURE INTERVIEWS** > - Talk Through the Trade-offs: Because you have strong high-level instincts, use that to your advantage. Talk through your brute-force ideas out loud and analyze their runtimes immediately. This will help you spot scaling flaws (like the hour-by-hour approach) before you start typing. > - Slow Down at the Start: Spend the first 3 to 5 minutes of every problem doing nothing but asking questions, writing down constraints, and sketching out 2 or 3 small test cases (including edge cases). > - Practice Implementation Constraints: To iron out the struggles with conditional logic and implementation speed, practice coding under a timer. Focus on translation—moving from your high-level conceptual understanding to working code without hesitation. > **OPEN NOTES AND RESOURCES** > - To build a rock-solid foundation on common interview patterns and data structures, I highly recommend checking out the book "Beyond Cracking the Coding Interview" by Gayle Laakmann McDowell, Mike Mroczka, Aline Lerner, and Nil Mamano. It is an excellent first-preference resource for mastering problem sets and recognizing patterns on the fly. > - For the Rental Car problem specifically, look into the "Interval Scheduling" or "Meeting Rooms" pattern. Practicing problems that utilize Heaps (Priority Queues) and the Two-Pointer/Sorting technique for intervals will directly help you master the conditional logic required for scheduling and allocation problems. ### Interview Transcript Lexical Panda: Good afternoon. Hello. How are you doing? Wily Tornado: I'm doing quite well. How are you, my friend? Lexical Panda: I'm doing well as well. Wily Tornado: Excellent, excellent. Wellness established, I suppose. Lexical Panda: I'm sorry? Wily Tornado: Wellness has been established. Lexical Panda: It has been established, of course. Wily Tornado: course. Lexical Panda: So, uh, just to get started, um, what is your objective? What are you looking to get out of this session? Wily Tornado: Um, oh, um, I want to get better at, um, just programming on the spot. I have a tendency to freeze in actual interviews. I want to work through that. Lexical Panda: Got it. Wily Tornado: And I'll also say that sometimes I know a problem and I know what to do high level, but when I start writing the code, I get mixed up with the conditional logic. ... Wily Tornado: I don't have any other questions other than like, you know, general advice, what to work on, any tips or hints, stuff like that, but no questions come up for this problem. Lexical Panda: Yeah, honestly, uh, your logic is very sound, right? Your, um, thinking process is very structured. Communication is great. I think you just need more practice. And, uh, another thing that I would say is, uh, read up on data structures. An algorithm at the most fundamental level, right? Regardless of the problem. What has helped me is just recognize some patterns and know the algorithms like DFS, BFS, heap, hash, stacks, List, uh, recursion particularly. Wily Tornado: Yeah. Lexical Panda: Cool. I hope this helped you. Wily Tornado: I feel like this is tremendously helpful. Thank you very much. Lexical Panda: No, thank you so much for your time. And I'll do a detailed write-up for you as well. So. Wily Tornado: Great. Thanks a lot. Lexical Panda: I'll be sharing this. in half an hour, maybe. Wily Tornado: Nice.