REST API serving with FastAPI in MLOps - Practice Problems & Coding Challenges

Challenge - 5 Problems

💻 Command Output

intermediate

What is the output of this FastAPI endpoint response?

Given this FastAPI endpoint code, what will be the JSON response when a GET request is made to /items/42?

MLOps

from fastapi import FastAPI
app = FastAPI()

@app.get('/items/{item_id}')
async def read_item(item_id: int):
    return {"item_id": item_id, "name": f"Item {item_id}"}

A {"item_id": 42, "name": "item 42"}

B {"item_id": "42", "name": "Item 42"}

C {"item_id": 42, "name": "Item 42"}

D {"item_id": 42}

💡 Hint

Remember that FastAPI converts path parameters to the declared type.

The endpoint returns a JSON with item_id as an integer and name as a string with capital 'I'. Option C matches exactly.

❓ Configuration

intermediate

Which uvicorn command correctly runs the FastAPI app from main.py with auto-reload?

You have a FastAPI app in main.py with the app instance named app. Which command will start the server with auto-reload enabled?

A uvicorn main.py:app --reload

B uvicorn app:main --reload

C uvicorn main.app --reload

D uvicorn main:app --reload

💡 Hint

The syntax is uvicorn module_name:app_instance.

The correct syntax is uvicorn main:app --reload. The module name is main without the .py extension, and app is the FastAPI instance.

🔀 Workflow

advanced

What is the correct sequence to deploy a FastAPI app with Docker?

Arrange these steps in the correct order to build and run a FastAPI app using Docker.

A 1,2,3,4

B 2,1,3,4

C 1,3,2,4

D 3,2,1,4

💡 Hint

Think about writing the Dockerfile before building the image.

You must first write the Dockerfile, then build the image, run the container, and finally test the API.

❓ Troubleshoot

advanced

Why does this FastAPI app raise a 422 error on POST?

This FastAPI endpoint expects JSON with name (string) and age (int). The client sends {"name": "Alice", "age": "25"}. Why does the server respond with status 422?

MLOps

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class User(BaseModel):
    name: str
    age: int

@app.post('/users')
async def create_user(user: User):
    return user

A Because age is sent as a string, not an integer

B Because name is missing in the JSON

C Because the endpoint only accepts GET requests

D Because the JSON is missing required fields

💡 Hint

Check the data types expected by Pydantic models.

Pydantic expects age as an integer, but the client sent it as a string, causing validation to fail and return 422.

✅ Best Practice

expert

Which approach best improves FastAPI app performance under high load?

You want to improve the performance of a FastAPI app serving ML model predictions under heavy traffic. Which option is the best practice?

A Use a single uvicorn worker to avoid race conditions

B Use Gunicorn with multiple workers and uvicorn workers to handle concurrency

C Disable async features to simplify code

D Run uvicorn with --reload for faster code changes in production

💡 Hint

Consider how to handle many requests efficiently in production.

Gunicorn with multiple workers and uvicorn workers allows handling many requests concurrently, improving performance under load.