The setup itself wasn't too bad to understand but the codebase context made everything slower.
Start by clarifying the problem constraints and game mechanics, then propose a greedy strategy with a priority queue to always pick the highest-scoring set, and discuss how to integrate it into the existing codebase with minimal disruption. Finally, analyze trade-offs and potential optimizations.
Pro tip: Demonstrate awareness of the existing codebase by suggesting to first locate and understand the current game loop and card representation before designing the strategy. This shows you value maintainability and integration over just algorithmic prowess.
Ask questions to understand the game rules, scoring, deck size, card values, and replenishment mechanics. Confirm whether the goal is to maximize score for a single player or if there are opponents.
Propose a greedy approach: at each turn, find all valid sets of 3 cards summing to 15, choose the one with highest immediate score (if scores vary), and remove those cards. Use a priority queue or sorted structure to efficiently retrieve the best set.
Identify where the game loop and card management reside. Suggest adding a new strategy module that adheres to existing interfaces, and use dependency injection or strategy pattern to plug it in without modifying core logic.
Discuss time/space complexity, potential for lookahead or dynamic programming if greedy is suboptimal, and how to handle ties or edge cases. Mention testing and validation against existing tests.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the problem context and constraints first, then analyze whether the order of playing disjoint sets affects the final outcome or intermediate states. Discuss trade-offs between different strategies (e.g., greedy vs. optimal) and justify your choice based on the problem's requirements.
Pro tip: In many cases, the order doesn't matter if the sets are truly disjoint and the goal is just to play all valid sets. However, if there are side effects or resource constraints, order can matter. Always ask clarifying questions to understand the problem fully.
Ask questions to understand the rules: What defines a valid set? Are there constraints like limited moves or time? What is the objective (e.g., maximize score, minimize moves)?
Determine if the sets are truly independent. If playing one set affects the availability or validity of another, order matters. Otherwise, it may not.
Identify any side effects of playing a set, such as changing the game state, consuming resources, or triggering events. These can make order important.
If order matters, discuss possible strategies (e.g., greedy, heuristic, exhaustive search) and their trade-offs in terms of time complexity and optimality.
Based on the analysis, recommend whether order matters and, if so, which strategy to use. Justify with reasoning about correctness and efficiency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on the branching factor math.
Start by clarifying the problem constraints (input size, time/memory limits, branching factor) and then derive a depth bound using complexity analysis. Justify the cutoff by balancing worst-case resource usage against expected solution quality, and mention pruning or iterative deepening as ways to extend effective depth.
Pro tip: Quantify the trade-off with concrete numbers (e.g., 'At depth 10 with branching factor 3, we'd explore ~59k nodes, which fits in 100ms') to show you think in terms of real system limits, not just theory.
Ask about input size, time/memory limits, branching factor, and whether the search is exhaustive or heuristic. State any assumptions you make.
Express the number of nodes as a function of depth (e.g., O(b^d)) and relate it to the available time and memory budget.
Solve for the maximum depth that fits within the budget, considering worst-case and average-case scenarios.
Explain why that depth is acceptable: it balances solution quality, latency, and resource usage. Mention alternatives like pruning or iterative deepening.
Propose empirical testing or profiling to confirm the cutoff, and describe how you'd adjust it if constraints change.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.