REST API serving with FastAPI in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import FastAPI and create an app instance.

MLOps

from fastapi import [1]

app = [1]()

Drag options to blanks, or click blank then click option'

ADepends

BRequest

CResponse

DFastAPI

Attempts:

3 left

💡 Hint

Common Mistakes

Importing other classes like Request instead of FastAPI.

Forgetting to call FastAPI() to create the app instance.

We import FastAPI and create an app instance by calling FastAPI().

2fill in blank

medium

Complete the code to define a GET endpoint at path '/' that returns a welcome message.

MLOps

@app.[1]("/")
def read_root():
    return {"message": "Welcome to FastAPI!"}

Drag options to blanks, or click blank then click option'

Apost

Bput

Cget

Ddelete

Attempts:

3 left

💡 Hint

Common Mistakes

Using POST or PUT decorators for a simple read endpoint.

Misspelling the decorator name.

The @app.get decorator defines a GET HTTP method endpoint.

3fill in blank

hard

Fix the error in the code to correctly accept a path parameter 'item_id' as an integer.

MLOps

@app.get("/items/{item_id}")
def read_item(item_id: [1]):
    return {"item_id": item_id}

Drag options to blanks, or click blank then click option'

Aint

Bfloat

Cstr

Dbool

Attempts:

3 left

💡 Hint

Common Mistakes

Using string type which accepts anything, losing validation.

Using float or bool which do not match the expected parameter.

Path parameters can be typed. Here, item_id should be an integer, so use int.

4fill in blank

hard

Fill both blanks to define a POST endpoint '/items/' that accepts a JSON body with a Pydantic model 'Item'.

MLOps

from pydantic import BaseModel

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

@app.[1]("/items/")
def create_item(item: [2]):
    return item

Drag options to blanks, or click blank then click option'

Apost

Bget

CItem

Dstr

Attempts:

3 left

💡 Hint

Common Mistakes

Using GET decorator for POST endpoint.

Typing the parameter as string instead of the model.

Use @app.post to define a POST endpoint and type the parameter as the Pydantic model Item to accept JSON body.

5fill in blank

hard

Fill all three blanks to run the FastAPI app with uvicorn on host '127.0.0.1' and port 8000.

MLOps

import uvicorn

if __name__ == "__main__":
    uvicorn.run([1], host=[2], port=[3])

Drag options to blanks, or click blank then click option'

A"app:app"

B"0.0.0.0"

C8000

D"127.0.0.1"

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong host like '0.0.0.0' for local-only run.

Passing port as string instead of integer.

Wrong app string format.

To run the app, use uvicorn.run("app:app", host="127.0.0.1", port=8000). The string "app:app" means module app and variable app.

Practice

1. What is the main purpose of using FastAPI in serving machine learning models?

easy

A. To train machine learning models faster

B. To create fast and simple REST APIs for model serving

C. To store large datasets efficiently

D. To visualize model performance graphs

Solution

1. Step 1: Understand FastAPI's role

   FastAPI is a web framework used to build APIs quickly and simply.

2. Step 2: Connect to model serving

   It is commonly used to serve machine learning models via REST APIs for easy access.

3. Final Answer:

   To create fast and simple REST APIs for model serving -> Option B

4. Quick Check:

   FastAPI = REST API serving [OK]

Hint: FastAPI is for building APIs, not training or storage [OK]

Common Mistakes:

• Confusing API serving with model training
• Thinking FastAPI handles data storage
• Assuming FastAPI is for visualization

2. Which of the following is the correct way to define a GET endpoint in FastAPI?

easy

A. @app.route('/items', method='GET')
def read_items():
return {'items': []}

B. @app.post('/items')
def read_items():
return {'items': []}

C. @app.get('/items')
def read_items():
print('items')

D. @app.get('/items')
def read_items():
return {'items': []}

Solution

1. Step 1: Identify correct decorator for GET

   FastAPI uses @app.get() to define GET endpoints.

2. Step 2: Check function returns JSON response

   The function should return a dictionary to send JSON; print statement does not return data.

3. Final Answer:

   @app.get('/items')\ndef read_items():\n return {'items': []} -> Option D

4. Quick Check:

   @app.get() + return dict = correct GET endpoint [OK]

Hint: Use @app.get() and return dict for GET endpoints [OK]

Common Mistakes:

• Using @app.post() for GET endpoints
• Using print instead of return
• Using Flask-style @app.route() syntax

3. What will be the output when you call the following FastAPI endpoint?

@app.get('/hello')
def say_hello():
    return {'message': 'Hello, FastAPI!'}

medium

A. {\"message\": \"Hello, FastAPI!\"}

B. Hello, FastAPI!

C. Error: Missing return type

D. 404 Not Found

Solution

1. Step 1: Analyze endpoint return value

   The function returns a dictionary with key 'message' and value 'Hello, FastAPI!'.

2. Step 2: Understand FastAPI response format

   FastAPI automatically converts dict to JSON response with the same structure.

3. Final Answer:

   {"message": "Hello, FastAPI!"} -> Option A

4. Quick Check:

   Return dict = JSON response with same keys [OK]

Hint: Return dict from endpoint gives JSON response [OK]

Common Mistakes:

• Expecting plain string instead of JSON
• Thinking missing return type causes error
• Assuming endpoint path is incorrect

4. Identify the error in this FastAPI POST endpoint code:

@app.post('/predict')
def predict(data: dict):
    return {'result': data['value'] * 2}

medium

A. Missing request body declaration with Pydantic model

B. Incorrect HTTP method, should be GET

C. Function missing return statement

D. Syntax error in decorator

Solution

1. Step 1: Check parameter type for POST data

   FastAPI requires request body to be declared with Pydantic models or Body for parsing JSON.

2. Step 2: Understand why dict alone is insufficient

   Using plain dict as parameter does not parse JSON body automatically, causing validation error.

3. Final Answer:

   Missing request body declaration with Pydantic model -> Option A

4. Quick Check:

   POST body needs Pydantic model or Body [OK]

Hint: Use Pydantic models for POST request bodies [OK]

Common Mistakes:

• Using plain dict instead of Pydantic model
• Confusing POST with GET method
• Forgetting to return a response

5. You want to serve a machine learning model prediction via FastAPI. Which approach correctly handles input validation and prediction?

from pydantic import BaseModel

class InputData(BaseModel):
    feature1: float
    feature2: float

@app.post('/predict')
def predict(data: InputData):
    result = model.predict([[data.feature1, data.feature2]])
    return {'prediction': result[0]}

What is the main advantage of this design?

hard

A. It skips input validation for faster response

B. It trains the model on each request

C. It validates input data types automatically before prediction

D. It returns raw model object instead of prediction

Solution

1. Step 1: Understand Pydantic model role

   InputData class validates that feature1 and feature2 are floats before function runs.

2. Step 2: Connect validation to prediction safety

   This prevents invalid data from reaching model.predict, avoiding runtime errors.

3. Final Answer:

   It validates input data types automatically before prediction -> Option C

4. Quick Check:

   Pydantic = automatic input validation [OK]

Hint: Use Pydantic models to validate inputs before prediction [OK]

Common Mistakes:

• Thinking model retrains on each request
• Skipping validation causes errors
• Returning model object instead of prediction