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Coding & Algorithms questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master coding & algorithms interviews.

What difficulty level is this interview question?

This is a medium difficulty Coding & Algorithms question, commonly asked during Technical Screen rounds at Google.

What role is this question designed for?

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

Choose optimal guesses for green-only Wordle

Q: Choose optimal guesses for green-only Wordle

This question evaluates probabilistic reasoning, expected-value optimization, information partitioning, and algorithmic efficiency in selecting guesses under partial (green-only) feedback; it belongs to the Coding & Algorithms domain, specifically search and probabilistic modeling, and emphasizes practical algorithm design and implementation rather than purely theoretical concepts. It is commonly asked to assess a candidate's ability to model uncertainty with weighted distributions, minimize the expected remaining search space, and manage time/space trade-offs for inputs up to several thousand words.

Problem

You are playing a simplified 5-letter word guessing game.

There is a hidden secret word of length 5 .
You have a dictionary of valid 5-letter words. Each word may also have an associated frequency/weight representing how likely it is to be the secret.
When you guess a 5-letter word, the game returns only “green” information (correct letter in the correct position). There is no information about letters that exist in the word but are in the wrong position (“yellow”), and no explicit info about letters that are absent.

Feedback definition

For a guess g and secret s, define the feedback as a 5-bit mask m where:

m[i] = 1 iff g[i] == s[i]
otherwise m[i] = 0

Example: g="crane", s="cared" → mask is 1 0 0 0 0 (only position 0 matches).

Task

Given:

words : a list of N valid 5-letter words
weight[w] (optional): a positive number for each word (if omitted, assume uniform)
candidates : the current set of possible secret words consistent with feedback so far (initially all words )

Write an algorithm/function to choose the next guess that is optimal under the following objective:

Choose a guess word g (typically from words ) that minimizes the expected size of the remaining candidate set after receiving the green-only mask, where the expectation is taken over the secret word drawn from candidates according to weight .

Formally, if C is the current candidate set and C_m(g) is the subset of candidates that would produce mask m for guess g, choose g that minimizes:

$\mathbb{E}[|C_{mask}(g)|] = \sum_{m \in \{0,1\}^5} P(mask=m) \cdot |C_m(g)|$

where $P(mask=m)$ is induced by the weighted distribution over secrets in C.

Input/Output expectations

Input sizes: 1 ≤ N ≤ 5,000 (you may propose optimizations if needed), word length is always 5.
Output: the selected next guess word.

Follow-ups (optional)

How would you incorporate non-uniform word frequencies?
How would you scale if N is very large (e.g., 100k)?
How would you change the objective to minimize expected number of guesses instead of expected remaining candidates?

Problem

You are playing a simplified 5-letter word guessing game.

There is a hidden secret word of length 5 .
You have a dictionary of valid 5-letter words. Each word may also have an associated frequency/weight representing how likely it is to be the secret.
When you guess a 5-letter word, the game returns only “green” information (correct letter in the correct position). There is no information about letters that exist in the word but are in the wrong position (“yellow”), and no explicit info about letters that are absent.

Feedback definition

For a guess g and secret s, define the feedback as a 5-bit mask m where:

m[i] = 1 iff g[i] == s[i]
otherwise m[i] = 0

Example: g="crane", s="cared" → mask is 1 0 0 0 0 (only position 0 matches).

Task

Given:

words : a list of N valid 5-letter words
weight[w] (optional): a positive number for each word (if omitted, assume uniform)
candidates : the current set of possible secret words consistent with feedback so far (initially all words )

Write an algorithm/function to choose the next guess that is optimal under the following objective:

Choose a guess word g (typically from words ) that minimizes the expected size of the remaining candidate set after receiving the green-only mask, where the expectation is taken over the secret word drawn from candidates according to weight .

Formally, if C is the current candidate set and C_m(g) is the subset of candidates that would produce mask m for guess g, choose g that minimizes:

$\mathbb{E}[|C_{mask}(g)|] = \sum_{m \in \{0,1\}^5} P(mask=m) \cdot |C_m(g)|$

where $P(mask=m)$ is induced by the weighted distribution over secrets in C.

Input/Output expectations

Input sizes: 1 ≤ N ≤ 5,000 (you may propose optimizations if needed), word length is always 5.
Output: the selected next guess word.

Follow-ups (optional)

How would you incorporate non-uniform word frequencies?
How would you scale if N is very large (e.g., 100k)?
How would you change the objective to minimize expected number of guesses instead of expected remaining candidates?

Choose optimal guesses for green-only Wordle

Quick Overview

Problem

Feedback definition

Task

Input/Output expectations

Follow-ups (optional)

Submit Your Answer to Earn 20XP

Choose optimal guesses for green-only Wordle

Quick Overview

Problem

Feedback definition

Task

Input/Output expectations

Follow-ups (optional)

Submit Your Answer to Earn 20XP