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 Technical Screen rounds at Salesforce.

What role is this question designed for?

This question is commonly asked for Software Engineer candidates at Salesforce during technical interviews.

Design a recommendation system | Salesforce Interview Question

Quick Overview

This question evaluates a candidate's competency in ML system design for recommendation systems, including choices of recommendation models, handling implicit feedback and cold-start, low-latency serving architecture, and storage/schema design for users, items, and interactions.

Design a User–Item Recommendation System

Context

You are asked to design an end-to-end recommendation service that suggests items to users. The service should include choices for the recommendation model, the serving architecture with multiple application servers behind a reverse proxy, and a storage schema for users, items, and interactions.

Assume: millions of users and items, primarily implicit feedback (views, clicks, purchases), and a p95 online latency target under 150 ms for the recommendation endpoint.

Tasks

Model choice and approach
- Explain a reasonable baseline (e.g., NMF/matrix factorization) and alternatives.
- Discuss handling implicit vs. explicit feedback, cold start, and ranking.
- Describe training cadence and evaluation.
Data fetching and serving architecture
- Describe how multiple stateless servers behind a reverse proxy will serve recommendations.
- Cover caching, timeouts/retries, fallbacks, and model/feature serving calls.
Data storage design
- Propose schemas for user, item, and interaction data.
- Include any derived tables (e.g., learned embeddings/factors) and indexing/partitioning choices.

Quick Overview

Context

Assume: millions of users and items, primarily implicit feedback (views, clicks, purchases), and a p95 online latency target under 150 ms for the recommendation endpoint.

Tasks

Model choice and approach

Explain a reasonable baseline (e.g., NMF/matrix factorization) and alternatives.
Discuss handling implicit vs. explicit feedback, cold start, and ranking.
Describe training cadence and evaluation.

Data fetching and serving architecture

Describe how multiple stateless servers behind a reverse proxy will serve recommendations.
Cover caching, timeouts/retries, fallbacks, and model/feature serving calls.

Data storage design

Propose schemas for user, item, and interaction data.
Include any derived tables (e.g., learned embeddings/factors) and indexing/partitioning choices.

Design a recommendation system

Quick Overview

Design a User–Item Recommendation System

Context

Tasks

Solution

Submit Your Answer to Earn 20XP

Design a recommendation system

Quick Overview

Design a User–Item Recommendation System

Context

Tasks

Solution

Submit Your Answer to Earn 20XP