Wordle Game: Implement Core Wordle Logic with Scoring and Guess Validation
Interview Experience
Problem
Implement the core logic for a Wordle-style word game. Given a 5-letter secret word and a 5-letter guess,
return a per-letter
result GREEN (correct position), YELLOW (letter in word but wrong position), GRAY (letter not in word). Handle duplicate letters correctly.
python
from enum import Enum
class Result(Enum):
GREEN = 'G'
YELLOW = 'Y'
GRAY = '_'
def score_guess(secret: str, guess: str) -> list[Result]:
# Both strings are exactly 5 uppercase letters
pass
def is_valid_guess(word: str, word_list: set[str]) -> bool:
pass
Example:
score_guess("CRANE", "CARES")
-> [G, Y, Y, _, Y]
# C: correct position; A: in word wrong pos; R: in word wrong pos;
# E: not present; S: in word wrong pos (wait -- check duplicate rules)
Follow-ups
- Explain how you handle duplicate letters: e.g., secret="HELLO", guess="LLAMA".
- How would you implement a solver that picks the guess minimizing expected remaining candidates?
- How would you extend this to support 6-letter words or hard mode (must use confirmed letters)?
- How would you store and query the word list to validate guesses in O(1)?
Full Details
Problem
Implement the core logic for a Wordle-style word game. Given a 5-letter secret word and a 5-letter guess,
return a per-letter
result GREEN (correct position), YELLOW (letter in word but wrong position), GRAY (letter not in word). Handle duplicate letters correctly.
python
from enum import Enum
class Result(Enum):
GREEN = 'G'
YELLOW = 'Y'
GRAY = '_'
def score_guess(secret: str, guess: str) -> list[Result]:
# Both strings are exactly 5 uppercase letters
pass
def is_valid_guess(word: str, word_list: set[str]) -> bool:
pass
Example:
score_guess("CRANE", "CARES")
-> [G, Y, Y, _, Y]
# C: correct position; A: in word wrong pos; R: in word wrong pos;
# E: not present; S: in word wrong pos (wait -- check duplicate rules)
Follow-ups
- Explain how you handle duplicate letters: e.g., secret="HELLO", guess="LLAMA".
- How would you implement a solver that picks the guess minimizing expected remaining candidates?
- How would you extend this to support 6-letter words or hard mode (must use confirmed letters)?
- How would you store and query the word list to validate guesses in O(1)?
About This Question
This is a candidate experience report from a verkada interview during the phone round.
More Verkada Interview Questions
About Verkada Interview Reports
This question was reported by a candidate who interviewed at Verkada. 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.
Use this question as one calibration data point, not a memorization target. Companies typically rotate their question pools every 2-4 months; the exact wording of a 2024 question may differ from what you encounter today. The underlying pattern, difficulty level, and follow-up depth at Verkada are the higher-signal extractions to take from this report.
For broader preparation context, the Verkada 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 Verkada reports consistently are the ones worth investing in; one-off niche problems are not.
During Your Verkada 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 Verkada 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.