FastAPI Training: 10 Sample Questions and Answers Explained

Key Concepts

Understanding FastAPI involves several key concepts:

• Routing: Defining endpoints and handling different HTTP methods.
• Request Body: Sending data to the server.
• Query Parameters: Passing data through the URL.
• Path Parameters: Extracting data from the URL path.
• Response Models: Defining the structure of the response.
• Dependency Injection: Managing dependencies in the application.
• Error Handling: Managing exceptions and providing meaningful error messages.
• Asynchronous Programming: Handling I/O-bound tasks efficiently.
• Security: Implementing authentication and authorization.
• Testing: Writing tests to ensure the application works as expected.

Sample Questions and Answers

1. How do you define a route in FastAPI?

A route in FastAPI is defined using decorators that specify the HTTP method and the path. For example:

from fastapi import FastAPI

app = FastAPI()

@app.get("/items/{item_id}")
async def read_item(item_id: int):
    return {"item_id": item_id}
    

2. How do you send data to the server using the request body?

Data can be sent to the server using the request body by defining a Pydantic model and using it as a parameter in the route function. For example:

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Item(BaseModel):
    name: str
    price: float

@app.post("/items/")
async def create_item(item: Item):
    return {"item": item}
    

3. How do you pass data through the URL using query parameters?

Query parameters are passed through the URL and can be accessed using the Query class. For example:

from fastapi import FastAPI, Query

app = FastAPI()

@app.get("/items/")
async def read_items(q: str = Query(None, min_length=3)):
    return {"q": q}
    

4. How do you extract data from the URL path using path parameters?

Path parameters are extracted from the URL path by defining them in the route decorator. For example:

from fastapi import FastAPI

app = FastAPI()

@app.get("/items/{item_id}")
async def read_item(item_id: int):
    return {"item_id": item_id}
    

5. How do you define the structure of the response using response models?

The structure of the response can be defined using Pydantic models. For example:

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Item(BaseModel):
    id: int
    name: str

@app.get("/items/{item_id}", response_model=Item)
async def read_item(item_id: int):
    return {"id": item_id, "name": "Sample Item"}
    

6. How do you manage dependencies in FastAPI using dependency injection?

Dependencies can be managed using the Depends function. For example:

from fastapi import FastAPI, Depends

app = FastAPI()

def get_db():
    db = "Database connection"
    try:
        yield db
    finally:
        db.close()

@app.get("/items/")
async def read_items(db = Depends(get_db)):
    return {"db": db}
    

7. How do you handle exceptions and provide meaningful error messages in FastAPI?

Exceptions can be handled using the HTTPException class. For example:

from fastapi import FastAPI, HTTPException

app = FastAPI()

@app.get("/items/{item_id}")
async def read_item(item_id: int):
    if item_id < 0:
        raise HTTPException(status_code=400, detail="Item ID must be positive")
    return {"item_id": item_id}
    

8. How do you handle I/O-bound tasks efficiently using asynchronous programming in FastAPI?

Asynchronous programming can be used to handle I/O-bound tasks efficiently using the async and await keywords. For example:

from fastapi import FastAPI
import asyncio

app = FastAPI()

async def fetch_data():
    await asyncio.sleep(1)
    return {"data": "fetched"}

@app.get("/data")
async def get_data():
    result = await fetch_data()
    return result
    

9. How do you implement authentication and authorization in FastAPI?

Authentication and authorization can be implemented using OAuth2 and JWT. For example:

from fastapi import FastAPI, Depends, HTTPException
from fastapi.security import OAuth2PasswordBearer

app = FastAPI()
oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")

def get_current_user(token: str = Depends(oauth2_scheme)):
    if not token:
        raise HTTPException(status_code=401, detail="Not authenticated")
    return {"user": "authenticated_user"}

@app.get("/secure")
async def secure_endpoint(user: dict = Depends(get_current_user)):
    return {"message": "This is a secure endpoint", "user": user}
    

10. How do you write tests to ensure the application works as expected in FastAPI?

Tests can be written using the TestClient class. For example:

from fastapi.testclient import TestClient
from main import app

client = TestClient(app)

def test_read_item():
    response = client.get("/items/1")
    assert response.status_code == 200
    assert response.json() == {"item_id": 1}
    

Analogies

Think of defining a route as creating a map for a treasure hunt. Sending data to the server is like sending a message in a bottle. Passing data through the URL is like adding clues to a treasure map. Extracting data from the URL path is like finding the hidden treasure. Defining the structure of the response is like organizing the treasure chest. Managing dependencies is like assembling a team for the treasure hunt. Handling exceptions is like dealing with unexpected obstacles. Asynchronous programming is like having multiple teams searching for different treasures simultaneously. Implementing authentication and authorization is like having a secret password to unlock the treasure chest. Writing tests is like checking the treasure map for accuracy before starting the hunt.

By mastering these concepts, you can effectively build and test a FastAPI application, ensuring it is robust, maintainable, and efficient.