This tripped me up at first because my instinct was to question the test.
Start by running the failing test to observe the exact failure and understand the expected behavior. Then trace the code path from the test through the card game implementation to identify the missing branch in main, and implement the fix while ensuring all tests pass.
Pro tip: Before diving into code, articulate your debugging strategy out loud—interviewers value a systematic approach over random guessing. Also, consider edge cases like empty hands or invalid moves that might reveal the missing branch.
Run the failing test and examine the error message, stack trace, and expected vs. actual output to pinpoint where the logic diverges.
Follow the execution from the test through the card game functions to main, identifying all branches and conditions that could lead to the failure.
Compare the expected behavior with the actual code to find the unhandled case or missing conditional in main that causes the test to fail.
Add the missing branch with correct logic, then rerun the test to confirm it passes and check for regressions in other tests.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Greedy felt natural here so I didn't overthink the approach.
Start by clarifying the game rules and constraints, then outline a greedy algorithm that selects any valid group of 3 cards summing to 15 at each step. Discuss how you would validate the strategy by simulating multiple games and analyzing the score distribution, including potential pitfalls of greedy approaches.
Pro tip: Acknowledge that greedy may not be optimal and propose comparing against a brute-force or dynamic programming solution for small inputs to quantify the trade-off. This shows depth and avoids overclaiming.
Ask questions to understand the game: card values, deck composition, rules for picking groups, scoring, and what 'valid group' means. Confirm that the goal is to implement and validate a greedy strategy.
Propose a greedy approach: at each step, scan for any triple of cards summing to 15 and remove them. Discuss data structures (e.g., hash map for complements) to efficiently find such triples.
Describe how to code the greedy strategy and run multiple game simulations. Track scores and collect data on the distribution of outcomes.
Interpret the score distribution, discuss whether greedy is effective, and compare with alternative strategies (e.g., optimal via DP) to highlight trade-offs between simplicity and optimality.
Wrap up with key findings, potential improvements, and lessons learned about greedy algorithms in game contexts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty straightforward after the implementation was done.
Start by clarifying the game's rules and the scoring logic, then design a unit test that simulates 100 games using a deterministic or seeded random generator. Use a loop to run the simulations, count perfect scores (180), and assert the count is within an expected range or simply log it for verification.
Pro tip: Make the test deterministic by injecting a seeded random number generator or mocking randomness, so the test is repeatable and not flaky. Also, consider separating the simulation logic from the test to keep it clean and testable.
Ask questions to understand the game rules, scoring system, and what constitutes a perfect score of 180. Confirm if randomness is involved and how it should be handled in tests.
Outline the test: set up a loop to run 100 games, call the game simulation function, and track the count of perfect scores. Decide on assertions or logging.
Ensure the test is deterministic by seeding the random number generator or mocking random outcomes. This avoids flaky tests and makes results reproducible.
Write the test code, run the simulations, and assert the count is as expected (e.g., within a range or exactly a known value if seeded). Use appropriate testing framework constructs.
Consider edge cases, performance (100 games should be fast), and maintainability. Discuss potential improvements like parameterizing the number of games.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The DP part I could sketch conceptually but the state space blew up fast when I tried to formalize it.
Start by formalizing the game as a Markov Decision Process where the state is (current hand, remaining deck composition). Then define the value function as the maximum probability of achieving a perfect score from that state, and derive a recurrence that considers all possible sets to play and the resulting draws. Finally, explain why perfect guarantee is impossible due to randomness and adversarial deck ordering.
Pro tip: Emphasize that the DP state must capture the multiset of remaining cards, not just the count, because the probabilities of drawing specific cards depend on the exact composition. Also, mention that the optimal strategy may be non-greedy and require considering future draws.
Model the state as (current hand, remaining deck composition). The remaining deck should be represented as a multiset of card types to accurately compute draw probabilities.
Let V(hand, deck) be the maximum probability of achieving a perfect score from this state. The goal is to compute V(initial_hand, initial_deck).
For each possible set that can be formed from the hand, consider playing it: remove those cards, draw new cards from the deck according to the hypergeometric distribution, and then recursively compute the expected value. The recurrence is V = max over sets of sum over possible draws of P(draw) * V(new_hand, new_deck).
Base case: if the hand already contains a perfect set (or if perfect score is achieved), return 1; if no sets can be formed and deck is empty, return 0. Use memoization to avoid recomputing states.
Argue that because the deck order is random and the player cannot control which cards are drawn, there is always a non-zero probability of drawing cards that prevent a perfect score, regardless of strategy. Thus no strategy can guarantee a perfect score.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.