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What difficulty level is this interview question?

This is a medium difficulty Coding & Algorithms question, commonly asked during Onsite rounds at Meta.

What role is this question designed for?

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

Build a FastAPI summarization service | Meta Interview Question

Quick Overview

This question evaluates web API development and backend engineering competencies, including FastAPI usage, Python type hints, Pydantic-based request/response validation, error handling, in-memory state management, and modular router/service/model layering, and it belongs to the Coding & Algorithms category with a focus on backend web service and API design. It is commonly asked to assess practical ability to deliver production-ready REST endpoints with proper validation and error handling under constrained resources, and the level of abstraction required is primarily practical implementation with attention to architectural design.

Implement a minimal but production-ready FastAPI service for text summarization.

Requirements:

Expose an endpoint POST /summarize .
The request body is JSON: { "text": string } .
The response body is JSON: { "summary": string } .
The summarization logic can be a placeholder function; you do not need to call a real model.
Store each request and response pair in memory.

Engineering requirements:

Use a clean structure with separate router, service, and model layers.
Use type hints throughout.
Use Pydantic models for request and response validation.
Handle invalid input and unexpected errors properly.
Keep the code modular, readable, and simple.
Do not use a database; in-memory storage is sufficient.

What to provide:

Show the proposed file structure.
Then provide the code for each file.

Quick Overview

Implement a minimal but production-ready FastAPI service for text summarization.

Requirements:

Expose an endpoint POST /summarize .
The request body is JSON: { "text": string } .
The response body is JSON: { "summary": string } .
The summarization logic can be a placeholder function; you do not need to call a real model.
Store each request and response pair in memory.

Engineering requirements:

Use a clean structure with separate router, service, and model layers.
Use type hints throughout.
Use Pydantic models for request and response validation.
Handle invalid input and unexpected errors properly.
Keep the code modular, readable, and simple.
Do not use a database; in-memory storage is sufficient.

What to provide:

Show the proposed file structure.
Then provide the code for each file.

Build a FastAPI summarization service

Quick Overview

Submit Your Answer to Earn 20XP

Build a FastAPI summarization service

Quick Overview

Submit Your Answer to Earn 20XP