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

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Meta during technical interviews.

Identify Features for Fake News Detection on Facebook

Quick Overview

This question evaluates skills in feature engineering, model selection, and end-to-end machine-learning system design with attention to privacy-preserving user features, multimodal signals, and deployment constraints for flagging misinformation on a social platform.

Design a Machine-Learning System to Flag Fake News on Facebook

Scenario

An increase in fake news has been detected on the platform. You are asked to design an automated system that flags likely misinformation for downranking, warning labels, or human review.

Task

Propose a feature set and modeling approach for a machine-learning system that flags fake news posts. Your answer should cover:

Features
- Text/content features
- User/account features (privacy-preserving, aggregated)
- Network/graph/cascade features
- Temporal/dynamic features
- Optional: link/source credibility and multimodal (image/video) signals
Modeling approach
- Baselines and production model(s)
- How to fuse heterogeneous signals
- Training data strategy (labels, noise, class imbalance)
Evaluation
- Offline metrics and validation protocol
- Thresholding, calibration, and cost trade-offs
- Fairness and robustness checks
Concept drift and ongoing monitoring
- Drift detection and retraining strategy
- Human-in-the-loop and active learning

State any assumptions you make and note practical deployment constraints (latency, language coverage, privacy).

Quick Overview

Task

Propose a feature set and modeling approach for a machine-learning system that flags fake news posts. Your answer should cover:

Features

Text/content features
User/account features (privacy-preserving, aggregated)
Network/graph/cascade features
Temporal/dynamic features
Optional: link/source credibility and multimodal (image/video) signals

Modeling approach

Baselines and production model(s)
How to fuse heterogeneous signals
Training data strategy (labels, noise, class imbalance)

Evaluation

Offline metrics and validation protocol
Thresholding, calibration, and cost trade-offs
Fairness and robustness checks

Concept drift and ongoing monitoring

Drift detection and retraining strategy
Human-in-the-loop and active learning

State any assumptions you make and note practical deployment constraints (latency, language coverage, privacy).

Identify Features for Fake News Detection on Facebook

Quick Overview

Design a Machine-Learning System to Flag Fake News on Facebook

Scenario

Task

Solution

Submit Your Answer to Earn 20XP

Identify Features for Fake News Detection on Facebook

Quick Overview

Design a Machine-Learning System to Flag Fake News on Facebook

Scenario

Task

Solution

Submit Your Answer to Earn 20XP