How do I approach Machine Learning interview questions?

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 medium difficulty Machine Learning question, commonly asked during Onsite rounds at Meta.

What role is this question designed for?

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

Build harmful-content text classifier

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a candidate's competence in designing an end-to-end machine learning pipeline for binary text classification, covering data understanding and labeling quality, preprocessing, model selection and training, evaluation and thresholding, handling class imbalance and ambiguous labels, and deployment considerations including latency, monitoring, and safety. Commonly asked in the Machine Learning domain, it gauges both practical application skills and conceptual understanding by testing an engineer's ability to balance model performance, evaluation metrics, and operational constraints in real-world NLP and safety-sensitive systems.

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Build harmful-content text classifier

Task

Propose an end-to-end approach to train and evaluate a classifier (you may assume you can fine-tune a pretrained Transformer).

What to cover

Data understanding, labeling quality, and preprocessing
Model choice and training procedure
Evaluation metrics and thresholding
Handling class imbalance and ambiguous labels
Deployment considerations: latency, monitoring, safety/abuse, and model updates

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