## Round 1 - Coding ## Problem Given a social network as a list of friendships, recommend potential friends for a user. Recommendations are users who are not already friends with the target user but share the most mutual friends. Exclude the user themselves. ```python def recommend_friends(friendships: list[tuple[str,str]], user: str, top_k: int) -> list[str]: # returns top_k recommended users sorted by mutual friend count desc, # ties broken alphabetically ... ``` ## Example ``` friendships = [ ("Alice","Bob"),("Alice","Carol"),("Bob","David"), ("Carol","David"),("David","Eve"),("Alice","Eve") ] recommend_friends(friendships, "Alice", top_k=2) # Alice's friends: {Bob, Carol, Eve} # David: shares Bob, Carol, Eve -> 2 mutuals (Bob,Carol) [Eve shares but Alice-Eve exists] # Actually David shares Bob and Carol with Alice -> 2 mutuals # -> ["David", ...] ``` ## Follow-ups 1. What is the time complexity of your approach? Can you reduce it using adjacency sets? 2. How would you handle a directed graph (follow vs. friend) instead of an undirected one? 3. How would you extend recommendations to include second-degree connections with lower weight? 4. At scale (100M users), how do you compute mutual friend counts efficiently?

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Friends Recommendation: Suggest New Friends Based on Mutual Connection Count

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Round 1 - Coding Problem Given a social network as a list of friendships, recommend potential friends for a user. Recommendations are users who are not already friends with the target user but share the most mutual friends. Exclude the user themselves. Example Follow-ups What is the time complexity of your approach? Can you reduce it using adjacency sets? How would you handle a directed graph (follow vs. friend) instead of an undirected one? How would you extend recommendations to include second…

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About MongoDB Interview Reports

This question was reported by a candidate who interviewed at MongoDB. 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.

Use this question as one calibration data point, not a memorization target. Companies typically rotate their question pools every 2-4 months; the exact wording of a 2024 question may differ from what you encounter today. The underlying pattern, difficulty level, and follow-up depth at MongoDB are the higher-signal extractions to take from this report.

For broader preparation context, the MongoDB interview process typically includes a recruiter screen, one or two technical phone screens, and a 4-5 round on-site loop covering coding, system design (at L4+ levels), and behavioral. Reports tagged on LeakCode show the round-by-round distribution and typical difficulty calibration. To browse questions filtered by round type and seniority, use the company hub linked above.

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 MongoDB reports consistently are the ones worth investing in; one-off niche problems are not.

During Your MongoDB 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 MongoDB 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.