Configuration management in FastAPI - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ component_behavior

intermediate

What is the output of this FastAPI configuration code?

Consider this FastAPI app configuration using Pydantic BaseSettings. What will be the value of app.state.config.debug after running this code?

FastAPI

from fastapi import FastAPI
from pydantic import BaseSettings

class Settings(BaseSettings):
    debug: bool = False

app = FastAPI()

@app.on_event("startup")
async def startup_event():
    app.state.config = Settings(debug=True)

# After startup event, what is app.state.config.debug?

A False

B True

C None

D Raises AttributeError

💡 Hint

Think about when the startup event runs and how the Settings instance is created.

The startup_event runs when the app starts and sets app.state.config to a Settings instance with debug=True. So app.state.config.debug is True.

📝 Syntax

intermediate

Which option correctly loads environment variables into FastAPI settings?

Given this Pydantic settings class, which option correctly loads environment variables from a file named .env?

FastAPI

from pydantic import BaseSettings

class Settings(BaseSettings):
    app_name: str = "MyApp"
    debug: bool = False

# How to load .env file automatically?

A

class Settings(BaseSettings):
    def __init__(self):
        self.env_file = ".env"

B

class Settings(BaseSettings):
    env_file = ".env"

C

class Settings(BaseSettings):
    class Config:
        env_file = ".env"

D

class Settings(BaseSettings):
    env_path = ".env"

💡 Hint

Pydantic uses a nested Config class for settings like env_file.

To load environment variables from a file, Pydantic requires setting env_file inside a nested Config class.

❓ state_output

advanced

What is the value of config.app_name after this FastAPI app runs?

This FastAPI app uses a settings instance stored in app.state. What will config.app_name print?

FastAPI

from fastapi import FastAPI
from pydantic import BaseSettings

class Settings(BaseSettings):
    app_name: str = "DefaultApp"

app = FastAPI()

@app.on_event("startup")
async def startup():
    app.state.config = Settings(app_name="CustomApp")

config = app.state.config
print(config.app_name)

A AttributeError because app.state.config is not set yet

B CustomApp

C DefaultApp

D None

💡 Hint

Consider when app.state.config is set versus when config is assigned.

The assignment config = app.state.config happens before the startup event runs, so app.state.config does not exist yet. This causes an AttributeError.

🔧 Debug

advanced

Why does this FastAPI app fail to load environment variables?

This code tries to load environment variables but always uses default values. What is the cause?

FastAPI

from fastapi import FastAPI
from pydantic import BaseSettings

class Settings(BaseSettings):
    debug: bool = False

    class Config:
        env_file = ".env"

app = FastAPI()

@app.on_event("startup")
async def startup():
    app.state.config = Settings()

# .env file contains DEBUG=true

A The startup event runs too late to load environment variables

B Pydantic BaseSettings does not support env_file

C The debug field must be uppercase to load from .env

D The .env file is not in the current working directory when app runs

💡 Hint

Check where the app runs and where the .env file is located.

Pydantic loads the .env file relative to the current working directory. If the .env file is not in that directory, variables won't load and defaults are used.

🧠 Conceptual

expert

Which is the best practice for managing configuration in a FastAPI app for multiple environments?

You want to manage different settings for development, testing, and production in FastAPI. Which approach is best?

A Use a single Pydantic BaseSettings class with environment variables loaded from different .env files per environment

B Use global variables and change them manually before running the app

C Hardcode all settings in the app code and comment/uncomment per environment

D Create separate FastAPI apps for each environment with duplicated code

💡 Hint

Think about maintainability and automation for different environments.

Using a single Pydantic BaseSettings class with environment variables loaded from different .env files per environment is the best practice. It keeps code clean and allows easy switching between environments.