The formula itself wasn't bad once I mapped out the three types separately.
First, clarify the type-specific rules for scale factor and comfort score, including how they combine into an overall score. Then, outline a modular implementation that processes each coaster, applies the appropriate rules, and rounds the result to one decimal place. Finally, discuss trade-offs such as extensibility and performance.
Pro tip: Emphasize writing clean, extensible code by using a strategy pattern or a dictionary of functions for each coaster type, which makes adding new types easy without modifying existing logic. Also, mention that you would validate inputs and handle edge cases like missing fields or unknown types gracefully.
Ask questions to confirm the exact formulas for scale factor and comfort score per type, and how they combine (e.g., product, sum, weighted average). Ensure you understand the rounding requirement.
Plan a function that takes a coaster description and returns a score. Use a dispatch mechanism (e.g., if-elif, dictionary mapping) to select type-specific logic for scale factor and comfort score.
For each coaster, extract type, max speed, bumps per second, and lift type. Apply the type-specific rules to compute scale factor and comfort score, then combine them into an overall score.
Round each overall score to one decimal place using appropriate rounding (e.g., round half up). Return the list of scores in the same order as input.
Walk through test cases including edge cases (e.g., zero bumps, unknown type). Discuss trade-offs like code maintainability, performance for large lists, and potential for parallelization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the interview actually got interesting.
Start by identifying the varying parts of the system (coaster types, scoring rules) and encapsulating them behind stable interfaces. Then show how the core processing logic depends only on abstractions, so new types or rules can be added via new classes or configuration without modifying existing code. Finally, discuss how this applies to ML systems, such as pluggable models or reward functions.
Pro tip: Emphasize that the open/closed principle is about managing dependencies, not eliminating them; use dependency inversion and clear extension points to keep the core stable while allowing flexibility.
Determine what aspects of the system are likely to change, such as coaster types or scoring rules, and separate them from the stable core logic.
Create interfaces or abstract classes that represent the varying behaviors, e.g., a CoasterType interface or a ScoringRule interface.
Make the core processing logic depend on these abstractions rather than concrete implementations, using dependency injection or a factory/registry pattern.
Add new coaster types or scoring rules by creating new classes that implement the interfaces, without modifying the core logic.
Provide a concrete example, such as adding a new coaster type or changing scoring, and explain how the core remains untouched.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.