Set Game: Implement the Card-Matching Logic for the Set Card Game
Question Details
Round 1 Coding / OOD
Problem
In the card game Set, each card has 4 attributes: number (1/2/3), color (red/green/purple), shading (solid/striped/open), and shape (diamond/squiggle/oval). Three cards form a valid Set if for each attribute, the values across the three cards are either all the same or all different.
python
from dataclasses import dataclass
@dataclass
class Card:
number: int # 1, 2, or 3
color: str
shading: str
shape: str
def is_valid_set(c1: Card, c2: Card, c3: Card) -> bool:
...
def find_all_sets(cards: list[Card]) -> list[tuple[Card, Card, Card]]:
...
Example
c1 = Card(1, "red", "solid", "diamond")
c2 = Card(2, "green", "striped", "squiggle")
c3 = Card(3, "purple", "open", "oval")
is_valid_set(c1, c2, c3) -> True # all different on every attribute
c4 = Card(1, "red", "solid", "oval")
c5 = Card(2, "green", "striped", "oval")
c6 = Card(3, "red", "open", "oval") # color: red/green/red -> invalid
is_valid_set(c4, c5, c6) -> False
Follow-ups
- What is the time complexity of
find_all_setsfor a board ofNcards? Can you do better than O(N^3)? - How do you generate all 81 unique cards in the standard deck?
- How would you detect when no valid Set exists on the current board (signaling the need to deal more cards)?
- How would you design a solver that finds a Set in the minimum number of steps for a given board state?
Full Details
Round 1 Coding / OOD
Problem
In the card game Set, each card has 4 attributes: number (1/2/3), color (red/green/purple), shading (solid/striped/open), and shape (diamond/squiggle/oval). Three cards form a valid Set if for each attribute, the values across the three cards are either all the same or all different.
python
from dataclasses import dataclass
@dataclass
class Card:
number: int # 1, 2, or 3
color: str
shading: str
shape: str
def is_valid_set(c1: Card, c2: Card, c3: Card) -> bool:
...
def find_all_sets(cards: list[Card]) -> list[tuple[Card, Card, Card]]:
...
Example
c1 = Card(1, "red", "solid", "diamond")
c2 = Card(2, "green", "striped", "squiggle")
c3 = Card(3, "purple", "open", "oval")
is_valid_set(c1, c2, c3) -> True # all different on every attribute
c4 = Card(1, "red", "solid", "oval")
c5 = Card(2, "green", "striped", "oval")
c6 = Card(3, "red", "open", "oval") # color: red/green/red -> invalid
is_valid_set(c4, c5, c6) -> False
Follow-ups
- What is the time complexity of
find_all_setsfor a board ofNcards? Can you do better than O(N^3)? - How do you generate all 81 unique cards in the standard deck?
- How would you detect when no valid Set exists on the current board (signaling the need to deal more cards)?
- How would you design a solver that finds a Set in the minimum number of steps for a given board state?
About This Question
This is a reported interview question from a samsara interview during the phone round.
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This question was reported by a candidate who interviewed at Samsara. LeakCode aggregates interview reports from 10+ sources, including 1Point3Acres, Glassdoor, LeetCode Discuss, Blind, Reddit, Indeed, and Nowcoder. Each report is translated where necessary, deduplicated against existing entries, and tagged by company, role, round type, and reporting date.
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For broader preparation context, the Samsara interview process typically includes a recruiter screen, one or two technical phone screens, and a 4-5 round on-site loop covering coding, system design (at L4+ levels), and behavioral. Reports tagged on LeakCode show the round-by-round distribution and typical difficulty calibration. To browse questions filtered by round type and seniority, use the company hub linked above.
How To Practice This Type of Question
Solve similar problems on LeetCode under timed conditions (25-35 minutes per medium difficulty). The goal is pattern recognition: recognize the underlying technique (sliding window, two-pointer, BFS, memoized recursion, etc.) within 60-90 seconds of reading. Strong candidates verbalize their hypothesis out loud before coding, then iterate based on feedback. Weak candidates dive into implementation immediately, lose time on the wrong approach, and run out of time for follow-ups.
Companies update their question pools every 2-4 months. The exact wording of any given question may have been retired by the time you interview. Focus your prep on the pattern, not the specific problem. The patterns that appear in Samsara reports consistently are the ones worth investing in; one-off niche problems are not.
During Your Samsara Round
Apply the standard interview round template: clarify requirements (2-3 minutes), state your approach out loud and confirm direction with the interviewer (3-5 minutes), code with narration (15-25 minutes), test with concrete examples including edge cases (5 minutes), discuss optimization or trade-offs if time permits (5 minutes). This template is universally accepted across FAANG and adjacent companies; deviating from it produces weaker interviewer feedback signal.
The single most predictive failure mode in Samsara reports tagged "no hire": not asking clarifying questions. Interviewers are explicitly trained to weight this. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's written notes.