## Round 1 - Coding / ML ## Problem Build an n-gram language model from a training corpus. The model should: 1. Count all n-grams of a given order `n`. 2. Given a context of `n-1` words, return the most likely next word. 3. Generate a sequence of `k` words starting from a seed context. ```python class NGramModel: def __init__(self, n: int): pass def train(self, corpus: list[str]) -> None: # corpus: list of sentences (strings) pass def predict_next(self, context: list[str]) -> str: # context: last n-1 words; returns most likely next word pass def generate(self, seed: list[str], k: int) -> list[str]: # Returns seed + k predicted words pass ``` ## Example ``` model = NGramModel(n=2) model.train(["the cat sat", "the cat ate", "the dog sat"]) model.predict_next(["the"]) # -> "cat" (highest count after "the") model.generate(["the"], 3) # -> ["the", "cat", "sat", ...] ``` ## Follow-ups 1. What is the sparsity problem with high-order n-grams? How do smoothing techniques (Laplace, Kneser-Ney) address it? 2. How do you evaluate model quality using perplexity on a held-out test set? 3. How does n-gram order affect fluency vs. diversity in generated text? 4. How would you extend this to sample probabilistically instead of always taking the argmax?

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N-gram - Build an N-gram Language Model for Text Generation

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Round 1 - Coding / ML Problem Build an n-gram language model from a training corpus. The model should: Count all n-grams of a given order n. Given a context of n-1 words, return the most likely next word. Generate a sequence of k words starting from a seed context. Example Follow-ups What is the sparsity problem with high-order n-grams? How do smoothing techniques (Laplace, Kneser-Ney) address it? How do you evaluate model quality using perplexity on a held-out test set? How does n-gram order af…

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About This Question

This is a candidate experience report from a way interview during the phone round.

It covers the following topics: Coding, Phone, Mle, Strings .

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About Way Interview Reports

This question was reported by a candidate who interviewed at Way. 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 Way are the higher-signal extractions to take from this report.

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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 Way reports consistently are the ones worth investing in; one-off niche problems are not.

During Your Way 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 Way 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.