Coram AI Full Stack and System Design Interview Experience
Question Details
Part 1: Coding Challenge - Wall Validation
Problem Statement Develop a function to validate a "wall" structure composed of 2D video tiles. The function receives a list of integers representing t
Full Details
Part 1: Coding Challenge - Wall Validation
Problem Statement Develop a function to validate a "wall" structure composed of 2D video tiles. The function receives a list of integers representing the (x, y) top-left coordinates of fixed-size 1x1 tiles.
Validation Criteria The function must return true if all the following conditions are met, and false otherwise: 1.
Non-Empty: The wall contains at least one tile. 2.
Origin: The shape starts at coordinate [0,0]. 3.
Shape: The tiles form a solid square or rectangle with no holes. 4.
Uniqueness: No tiles overlap (coordinates must be unique).
Input/Output Examples *
Valid: [[0,0], [0,1], [1,0], [1,1]] (2x2 Square) *
Valid: [[1,1], [0,1], [0,0], [1,0]] (Unsorted input is acceptable) *
Valid: [[0,0], [0,1]] (1x2 Rectangle) *
Invalid: [[0,0], [1,1]] (Contains holes) *
Invalid: [[0,0], [0,1], [1,0]] (Not a perfect rectangle) *
Invalid: [[0,0], [0,1], [0,1], [1,1]] (Contains overlaps) *** # Part 2:
System Design Camera Detection Events
Role: Full Stack Engineer
Data Context The system processes batched detection events from cameras running AI algorithms. *
Event Data: Camera hash (string), Detection type (Person, car, bicycle), Bounding Box (startX, startY, endX, endY), Timestamp. *
Existing Camera Metadata: Camera hash, Camera name, Last seen time.
Backend Requirements Design an architecture to ingest and process detection data with the following considerations: *
Ingestion: Handle batched events sent from cameras (e.g., every second). *
Storage: Persist detection events. *
Architecture: Define communication protocols, redundancy strategies, and scaling capabilities.
Frontend Requirements Design the following user interfaces: 1.
Landing Page (Camera List) * List all cameras in the system. * Display "Online" status based on last_seen_time (Online if seen < 30 seconds ago). * Handle dynamic updates (cameras added/removed). * Link to the Daily Activity page. 2.
Camera Daily Activity Page * Display detection analytics for a specific camera on a specific day. *
Required Metrics/Visualizations: * Timestamps for the first and last detection of each type (person/car/bike). * Distribution of detections per type throughout the day. * Concurrency analysis (e.g., identifying times when at least 4 people were detected simultaneously).
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About Coram AI Interview Reports
This question was reported by a candidate who interviewed at Coram AI. 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 Coram AI are the higher-signal extractions to take from this report.
For broader preparation context, the Coram AI 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 Coram AI reports consistently are the ones worth investing in; one-off niche problems are not.
During Your Coram AI 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 Coram AI 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.