Spent a decent chunk of time on this one and I'm still not sure how I did.
Start by clarifying the problem scope and constraints, then model the factory as a graph with nodes (machines, storage) and edges (conveyors, paths). Design a system that combines pathfinding (e.g., A*) for navigation and a scheduling algorithm for resource management, ensuring scalability and robustness.
Pro tip: Demonstrate awareness of real-world constraints like concurrency and fault tolerance, and discuss how you would test and monitor the simulation to ensure correctness and performance.
Ask questions to understand the factory layout, objectives (e.g., maximize throughput), and constraints (e.g., time, resources). Confirm input/output formats and performance expectations.
Represent the factory as a graph with nodes for machines/storage and edges for paths. Define state variables (e.g., machine status, inventory) and transitions.
Choose pathfinding algorithms (e.g., A*, Dijkstra) for navigation and scheduling algorithms (e.g., priority queues, round-robin) for task allocation. Consider optimization techniques like caching or heuristics.
Write modular code with clear interfaces. Simulate the factory step-by-step, handling events and updating state. Use appropriate data structures for efficiency.
Validate with unit tests and edge cases. Profile performance and optimize bottlenecks. Discuss scalability and potential improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem: define the map representation, drone state, movement commands, and test conditions. Then design modular functions for navigation, obstacle avoidance, and pathfinding, and outline how you would test them with unit tests and edge cases. Emphasize clean code, algorithmic efficiency, and robustness.
Pro tip: Demonstrate test-driven development by writing tests first for each function, and discuss how you would simulate the drone's environment to validate behavior under various conditions. This shows you prioritize correctness and maintainability.
Ask questions to understand the map format, drone capabilities, movement rules, and what 'test conditions' entail. Confirm expected inputs, outputs, and constraints.
Choose representations for the map (e.g., 2D grid, graph), drone state (position, direction, battery), and commands. Consider using classes or structs for clarity.
Write functions for moving the drone, detecting obstacles, and finding paths (e.g., BFS, A*). Ensure functions are modular and handle edge cases like boundaries.
Simulate the drone's navigation using the functions, and write unit tests for each function. Test scenarios like empty maps, obstacles, and unreachable destinations.
Explain choices like algorithm selection (BFS vs A*), memory vs speed, and how you would optimize for large maps. Mention potential improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem constraints and expected input/output for both matrix questions. For the first, implement a straightforward traversal or manipulation using nested loops, then for the second, identify inefficiencies (e.g., repeated scans) and optimize using techniques like in-place marking, boundary tracking, or dynamic programming. Communicate your thought process clearly, and test with edge cases like empty matrices or single rows/columns.
Pro tip: At Roblox, interviewers value clean, maintainable code and the ability to discuss trade-offs. After optimizing, briefly mention the time/space complexity improvement and any potential side effects (e.g., mutating input) to show you consider real-world impact.
Ask about matrix dimensions, data types, allowed auxiliary space, and whether the input can be modified. Confirm expected output format and edge cases.
Implement a clear, correct solution using nested loops or standard traversal patterns. Explain the logic step-by-step and analyze time/space complexity.
For the second problem, start with a naive solution, then pinpoint bottlenecks such as repeated passes or excessive memory usage. Discuss potential optimization strategies.
Apply an optimized technique (e.g., in-place marking, boundary shrinking, or DP) and code it cleanly. Explain how it improves complexity and why it works.
Walk through test cases including edge cases, and compare the naive vs. optimized solutions in terms of time, space, and readability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.