Detect robot state after movements

Quick Overview

This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Detect robot state after movements states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Detect robot state after movements

Implement an algorithm to determine whether a robot following a given set of movement instructions ends in a valid recognized state (variant of a robot-recognition problem); optimize to pass all edge-case tests.

Clarifying Questions to Ask Guidance

Clarify input sizes, value ranges, mutability, return format, and tie-breaking.
State the target time and space complexity before coding.
Call out edge cases such as empty inputs, duplicates, invalid values, overflow, and boundary sizes.

What a Strong Answer Covers Guidance

A clear algorithm with the right data structures and enough pseudocode or code-level detail to implement it.
A correctness argument that explains why the algorithm covers all required cases.
Time and space complexity, plus at least one alternative approach when relevant.
Focused tests for normal cases, edge cases, and failure modes.

Follow-up Questions Guidance

How would the approach change if the input were streaming or too large for memory?
What invariants would you assert in production code?
Which tests would catch off-by-one, duplicate, or tie-breaking bugs?

Detect robot state after movements

Quick Overview

Detect robot state after movements

Detect robot state after movements

Clarifying Questions to Ask Guidance

What a Strong Answer Covers Guidance

Follow-up Questions Guidance

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