Detect sequence rule and repair anomaly
Company: Coinbase
Role: Data Scientist
Category: Coding & Algorithms
Difficulty: Medium
Interview Round: HR Screen
Quick Answer: This question evaluates a candidate's skill in sequence pattern recognition, algorithm design, complexity analysis, and robust integer arithmetic by requiring inference among arithmetic, alternating-difference, geometric, and Fibonacci-plus-constant models while tolerating at most one corrupted term under O(n) time and O(1) extra space constraints.
Constraints
- Inputs are Python literals matching the function signature.
- Return a deterministic exact-match value.
Examples
Input: ([2,4,6,8],)
Expected Output: {'model': 'AP', 'parameters': {'a0': 2, 'd': 2}, 'anomaly_index': None, 'next_term': 10}
Explanation: Arithmetic progression.
Input: ([2,4,7,8],)
Expected Output: {'model': 'AP', 'parameters': {'a0': 2, 'd': 2}, 'anomaly_index': 2, 'next_term': 10}
Explanation: One AP anomaly.
Input: ([3,6,12,24],)
Expected Output: {'model': 'GP', 'parameters': {'a0': 3, 'r': 2}, 'anomaly_index': None, 'next_term': 48}
Explanation: Geometric progression.
Input: ([1,1,3,5,9],)
Expected Output: {'model': 'FibonacciPlusConstant', 'parameters': {'a0': 1, 'a1': 1, 'c': 1}, 'anomaly_index': None, 'next_term': 15}
Explanation: Fibonacci plus constant c=1.
Hints
- Use deterministic tie-breaking for prompts with multiple valid outputs.
- For design-style APIs, simulate operations with explicit inputs.