How do I approach ML System Design interview questions?

ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

What difficulty level is this interview question?

This is a medium difficulty ML System Design question, commonly asked during Onsite rounds at Databricks.

What role is this question designed for?

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

Design Harmful Content Detection | Databricks Interview Question

Design Harmful Content Detection

Last updated: Apr 22, 2026

Quick Overview

This question evaluates a candidate's ability to design scalable, robust machine learning systems for multimodal content moderation, encompassing competencies in system architecture, data labeling and governance, model evaluation, online inference, and monitoring.

Databricks

Feb 7, 2026, 12:00 AM

Machine Learning Engineer

Onsite

ML System Design

Design an end-to-end machine learning system to detect harmful user-generated content on a large online platform. Assume the platform accepts text and images, processes millions of submissions per day, and needs both low-latency online decisions and higher-quality offline review.

Your design should cover:

content taxonomy such as hate speech, threats, sexual content, self-harm, violent content, and spam,
model inputs and labeling strategy,
online inference and moderation workflows,
confidence thresholds and human review,
evaluation metrics,
monitoring, drift detection, and abuse resistance.

Solution

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