The core of the problem is the extensibility bit, not the movement math.
Start by clarifying requirements and constraints, then propose a modular design using the Command pattern to decouple execution from command definitions. Walk through the core components (Robot, Command interface, concrete commands, invoker) and explain how new commands can be added without modifying existing code. Finally, discuss trade-offs and potential extensions like undo or obstacle detection.
Pro tip: Emphasize that the design should be extensible and testable; mention how you would unit test each command and the robot's state transitions. Also, relate it to ML engineering by noting that similar patterns are used in ML pipelines for modularity and reproducibility.
Ask about grid size, initial position/direction, command set, and whether commands can be batched or undone. Confirm that the focus is on extensibility and clean separation of concerns.
Identify the main entities: Robot (holds position and direction), Command interface (with execute method), and concrete commands (MoveForward, RotateLeft, RotateRight). Consider using an invoker to execute commands.
Explain how the Command pattern allows new commands to be added by creating new classes that implement the Command interface, without modifying the robot or invoker. Show how commands encapsulate all information needed to perform an action.
Sketch the Robot class with methods to move and rotate, and the command classes that call these methods. Ensure the robot's state is updated correctly and that commands are decoupled from the robot's internal representation.
Talk about trade-offs: simplicity vs. flexibility, performance overhead of command objects, and potential for undo/redo. Mention extensions like obstacle detection, command parsing, and how this pattern scales.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through unit tests per command, then property-based stuff like 'four left rotations should return to the original direction.' The interviewer seemed to want me to mention edge cases around very long command strings (up to 100k), which I did get to eventually but not first.
Start by clarifying the module's purpose, inputs, outputs, and success metrics, then outline a layered testing strategy from unit to integration to end-to-end. After initial implementation, prioritize optimizations based on impact and cost, and systematically identify edge cases through data analysis and failure mode brainstorming.
Pro tip: Frame your answer around risk-based testing: focus on the highest-risk areas first, and tie optimizations to measurable business or user impact. This shows you can balance thoroughness with pragmatism, a key trait at Shopify.
Ask questions to understand the module's role, expected inputs/outputs, performance targets, and how it integrates with the larger system. Define what 'correct' and 'optimized' mean for this context.
Plan unit tests for individual functions, integration tests for component interactions, and end-to-end tests for real-world scenarios. Include data validation, model performance metrics, and regression tests.
Identify edge cases such as missing data, outliers, distribution shifts, and adversarial inputs. Use techniques like error analysis, data slicing, and stress testing to uncover them.
After initial implementation, profile the module to find bottlenecks (e.g., latency, memory, cost). Prioritize optimizations that improve key metrics like accuracy, speed, or scalability, and validate with A/B tests or offline evaluations.
Set up monitoring for data drift, model degradation, and system health. Use feedback loops to continuously refine tests and optimizations, ensuring long-term reliability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.