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Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

What difficulty level is this interview question?

This is a hard difficulty Machine Learning question, commonly asked during Technical Screen rounds at Runway.

What role is this question designed for?

This question is commonly asked for Machine Learning Engineer candidates at Runway during technical interviews.

Implement n-gram model and select n | Runway Interview Question

Quick Overview

This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Implement n-gram model and select n states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Implement n-gram model and select n

Task: Implement an n-gram Language Model with Training, Sampling, and Model Selection Guidance

Objective

Implement an n-gram language model class with the following methods and discuss model selection and complexity trade-offs:

fit(file_path, n):
- Read and tokenize a text file consistently.
- Build n-gram and (n−1)-gram frequency counts.
- Compute conditional probabilities with smoothing (e.g., add-k or Kneser–Ney).
generate(start_tokens, max_len, sampling_strategy):
- Sample next tokens according to learned probabilities (e.g., multinomial, top-k, temperature) to produce text.

Requirements and Notes

Tokenization must be consistent between training and generation. Include BOS/EOS handling if using sentence generation.
Smoothing options: implement at least add-k; explain and, if possible, implement interpolated Kneser–Ney.
Sampling strategies: support multinomial; add top-k and temperature scaling.
Model selection: discuss how to select the optimal n given data size and domain. Propose validation procedures (train/validation split), metrics (perplexity), regularization and backoff/interpolation.
Analyze time/space complexity and memory footprint for different n values.

Deliverables

Description of class design and data structures.
Clear pseudocode (or concise code sketch) for fit and generate.
Explanation of smoothing methods with formulas.
Explanation of sampling methods.
Strategy for choosing n with validation and perplexity.
Complexity and memory analysis.

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.
If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask Guidance

Clarify the task, data shape, labels, constraints, and evaluation metric.
State assumptions behind the math or modeling technique you choose.
Connect theory to practical training, debugging, and deployment implications.

What a Strong Answer Covers Guidance

Correct definitions and formulas where the prompt requires them.
A practical explanation of how the method behaves on real data.
Trade-offs, failure modes, diagnostics, and mitigation strategies.
Evaluation choices that match the product or modeling objective.

Follow-up Questions Guidance

How would noisy labels, class imbalance, or distribution shift affect the answer?
What would you monitor after deployment?
Which baseline would you compare against first?

Quick Overview

Objective

Implement an n-gram language model class with the following methods and discuss model selection and complexity trade-offs:

fit(file_path, n):

Read and tokenize a text file consistently.
Build n-gram and (n−1)-gram frequency counts.
Compute conditional probabilities with smoothing (e.g., add-k or Kneser–Ney).

generate(start_tokens, max_len, sampling_strategy):

Sample next tokens according to learned probabilities (e.g., multinomial, top-k, temperature) to produce text.

Requirements and Notes

Tokenization must be consistent between training and generation. Include BOS/EOS handling if using sentence generation.

Smoothing options: implement at least add-k; explain and, if possible, implement interpolated Kneser–Ney.

Sampling strategies: support multinomial; add top-k and temperature scaling.

Model selection: discuss how to select the optimal n given data size and domain. Propose validation procedures (train/validation split), metrics (perplexity), regularization and backoff/interpolation.

Analyze time/space complexity and memory footprint for different n values.

Deliverables

Description of class design and data structures.

Clear pseudocode (or concise code sketch) for fit and generate.

Explanation of smoothing methods with formulas.

Explanation of sampling methods.

Strategy for choosing n with validation and perplexity.

Complexity and memory analysis.

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.

If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.

Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask Guidance

Clarify the task, data shape, labels, constraints, and evaluation metric.

State assumptions behind the math or modeling technique you choose.

Connect theory to practical training, debugging, and deployment implications.

What a Strong Answer Covers Guidance

Correct definitions and formulas where the prompt requires them.

A practical explanation of how the method behaves on real data.

Trade-offs, failure modes, diagnostics, and mitigation strategies.

Evaluation choices that match the product or modeling objective.

Follow-up Questions Guidance

How would noisy labels, class imbalance, or distribution shift affect the answer?

What would you monitor after deployment?

Which baseline would you compare against first?

Implement n-gram model and select n

Quick Overview

Implement n-gram model and select n

Implement n-gram model and select n

Task: Implement an n-gram Language Model with Training, Sampling, and Model Selection Guidance

Objective

Requirements and Notes

Deliverables

Constraints & Assumptions

Clarifying Questions to Ask Guidance

What a Strong Answer Covers Guidance

Follow-up Questions Guidance

Write your answer

Implement n-gram model and select n

Quick Overview

Implement n-gram model and select n

Implement n-gram model and select n

Task: Implement an n-gram Language Model with Training, Sampling, and Model Selection Guidance

Objective

Requirements and Notes

Deliverables

Constraints & Assumptions

Clarifying Questions to Ask Guidance

What a Strong Answer Covers Guidance

Follow-up Questions Guidance

Write your answer