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System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master system design interviews.

What difficulty level is this interview question?

This is a medium difficulty System Design question, commonly asked during Technical Screen rounds at Nash AI.

What role is this question designed for?

This question is commonly asked for Software Engineer candidates at Nash AI during technical interviews.

Design OOD for probabilistic Minesweeper | Nash AI Interview Question

Quick Overview

Design OOD for probabilistic Minesweeper evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Design OOD for probabilistic Minesweeper

Object-Oriented Design: Minesweeper with Per-Cell Mine Probabilities

Context

Design an object-oriented Minesweeper game. The board is a grid of cells. On initialization, each cell is given a probability prob that it contains a mine. The system should use these probabilities to generate the mine layout and initialize cell states deterministically when needed (for testing) and randomly otherwise.

Assume prob can be provided as either:

A single scalar applied to all cells, or
A 2D grid of probabilities (same shape as the board).

Requirements

Explain how the per-cell probabilities are used to generate the initial mine layout and initialize cell states.
Define the core classes (e.g., Game, Board, Cell), their responsibilities, relationships, and public APIs to:
- (a) print the current board state
- (b) process a user click
Specify expected state transitions for a click at a high level (lose on mine; otherwise reveal logic handled by standard Minesweeper algorithms).
Describe handling of invalid or duplicate clicks.
Explain how the design supports maintainability and testing.

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.
If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask Guidance

Clarify users, core use cases, read/write patterns, scale, latency, availability, and data retention.
State explicit assumptions before making sizing or architecture decisions.
Prioritize the functional path first, then address reliability, security, observability, and rollout.

What a Strong Answer Covers Guidance

A scoped requirements summary with concrete non-goals and success metrics.
API, data model, architecture, consistency, capacity, and operations.
Reasoned trade-offs among simple and scalable designs, including bottlenecks and failure modes.
A validation, monitoring, migration, and launch plan appropriate for the risk level.

Follow-up Questions Guidance

What breaks first at 10x traffic or data volume?
How would you degrade gracefully during dependency failures?
What metrics and alerts would prove the design is healthy after launch?

Quick Overview

Context

Assume prob can be provided as either:

A single scalar applied to all cells, or

A 2D grid of probabilities (same shape as the board).

Requirements

Explain how the per-cell probabilities are used to generate the initial mine layout and initialize cell states.

Define the core classes (e.g., Game, Board, Cell), their responsibilities, relationships, and public APIs to:

(a) print the current board state
(b) process a user click

Specify expected state transitions for a click at a high level (lose on mine; otherwise reveal logic handled by standard Minesweeper algorithms).

Describe handling of invalid or duplicate clicks.

Explain how the design supports maintainability and testing.

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.

If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.

Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask Guidance

Clarify users, core use cases, read/write patterns, scale, latency, availability, and data retention.

State explicit assumptions before making sizing or architecture decisions.

Prioritize the functional path first, then address reliability, security, observability, and rollout.

What a Strong Answer Covers Guidance

A scoped requirements summary with concrete non-goals and success metrics.

API, data model, architecture, consistency, capacity, and operations.

Reasoned trade-offs among simple and scalable designs, including bottlenecks and failure modes.

A validation, monitoring, migration, and launch plan appropriate for the risk level.

Follow-up Questions Guidance

What breaks first at 10x traffic or data volume?

How would you degrade gracefully during dependency failures?

What metrics and alerts would prove the design is healthy after launch?

Design OOD for probabilistic Minesweeper

Quick Overview

Design OOD for probabilistic Minesweeper

Design OOD for probabilistic Minesweeper

Object-Oriented Design: Minesweeper with Per-Cell Mine Probabilities

Context

Requirements

Constraints & Assumptions

Clarifying Questions to Ask Guidance

What a Strong Answer Covers Guidance

Follow-up Questions Guidance

Submit Your Answer to Earn 20XP

Design OOD for probabilistic Minesweeper

Quick Overview

Design OOD for probabilistic Minesweeper

Design OOD for probabilistic Minesweeper

Object-Oriented Design: Minesweeper with Per-Cell Mine Probabilities

Context

Requirements

Constraints & Assumptions

Clarifying Questions to Ask Guidance

What a Strong Answer Covers Guidance

Follow-up Questions Guidance

Submit Your Answer to Earn 20XP