## Round 1 - Coding / OOD ## Problem Design a `UserLikesTracker` that records when users like or unlike content items, and supports efficient queries. Each like/unlike event has a user ID, item ID, and timestamp. ```python class UserLikesTracker: def like(self, user_id: str, item_id: str, ts: int) -> None: pass def unlike(self, user_id: str, item_id: str, ts: int) -> None: pass def is_liked(self, user_id: str, item_id: str) -> bool: pass def liked_items(self, user_id: str) -> list[str]: # Returns all item_ids currently liked by user, sorted by like timestamp desc pass def top_liked_items(self, k: int) -> list[tuple[str, int]]: # Returns top-k items by total current likes, desc pass ``` ## Example ``` tracker.like("u1", "post_a", ts=100) tracker.like("u2", "post_a", ts=101) tracker.like("u1", "post_b", ts=102) tracker.unlike("u1", "post_a", ts=103) tracker.is_liked("u1", "post_a") # -> False tracker.liked_items("u1") # -> ["post_b"] tracker.top_liked_items(1) # -> [("post_a", 1)] ``` ## Follow-ups 1. How do you make `top_liked_items` O(log k) instead of O(n log n) on each call? 2. How do you handle late-arriving events where unlike arrives before like due to network delays? 3. At 1M users and 100M items, how do you store like state efficiently in memory? 4. How do you expose a feed of "items liked by people you follow" with acceptable latency?

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User Likes Tracker - Efficiently Track and Query User Content Likes

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Round 1 - Coding / OOD Problem Design a UserLikesTracker that records when users like or unlike content items, and supports efficient queries. Each like/unlike event has a user ID, item ID, and timestamp. Example Follow-ups How do you make top_liked_items O(log k) instead of O(n log n) on each call? How do you handle late-arriving events where unlike arrives before like due to network delays? At 1M users and 100M items, how do you store like state efficiently in memory? How do you expose a feed…

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About This Question

This is a candidate experience report from a lead bank interview during the phone round.

It covers the following topics: Coding, Onsite, Phone .

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About Lead Bank Interview Reports

This question was reported by a candidate who interviewed at Lead Bank. LeakCode aggregates interview reports from 10+ sources, including 1Point3Acres, Glassdoor, LeetCode Discuss, Blind, Reddit, Indeed, and Nowcoder. Each report is translated where necessary, deduplicated against existing entries, and tagged by company, role, round type, and reporting date.

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How To Practice This Type of Question

Solve similar problems on LeetCode under timed conditions (25-35 minutes per medium difficulty). The goal is pattern recognition: recognize the underlying technique (sliding window, two-pointer, BFS, memoized recursion, etc.) within 60-90 seconds of reading. Strong candidates verbalize their hypothesis out loud before coding, then iterate based on feedback. Weak candidates dive into implementation immediately, lose time on the wrong approach, and run out of time for follow-ups.

Companies update their question pools every 2-4 months. The exact wording of any given question may have been retired by the time you interview. Focus your prep on the pattern, not the specific problem. The patterns that appear in Lead Bank reports consistently are the ones worth investing in; one-off niche problems are not.

During Your Lead Bank Round

Apply the standard interview round template: clarify requirements (2-3 minutes), state your approach out loud and confirm direction with the interviewer (3-5 minutes), code with narration (15-25 minutes), test with concrete examples including edge cases (5 minutes), discuss optimization or trade-offs if time permits (5 minutes). This template is universally accepted across FAANG and adjacent companies; deviating from it produces weaker interviewer feedback signal.

The single most predictive failure mode in Lead Bank reports tagged "no hire": not asking clarifying questions. Interviewers are explicitly trained to weight this. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's written notes.