FastAPI for model serving in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this FastAPI endpoint code?

Consider this FastAPI code snippet that serves a simple model prediction:

from fastapi import FastAPI
app = FastAPI()

@app.get('/predict')
async def predict(x: int):
    return {'prediction': x * 2}

What will be the JSON response when a client requests /predict?x=3?

ML Python

from fastapi import FastAPI
app = FastAPI()

@app.get('/predict')
async def predict(x: int):
    return {'prediction': x * 2}

A {"prediction": 6}

B {"prediction": "6"}

C {"prediction": null}

D {"prediction": 3}

💡 Hint

Remember that the endpoint multiplies the input x by 2 and returns it as an integer in JSON.

The function receives x=3, multiplies it by 2, and returns {'prediction': 6} as JSON. The value is an integer, not a string or null.

❓ Model Choice

intermediate

Which model loading method is best for FastAPI startup?

You want to load a machine learning model once when the FastAPI app starts, so it can be reused for all requests efficiently.

Which method below correctly loads the model at startup?

A Load the model inside the endpoint function so it reloads on every request.

B Load the model globally outside any function, so it loads once when the app starts.

C Load the model inside a dependency function with <code>Depends</code> that runs per request.

D Load the model inside a background task that runs after each request.

💡 Hint

Think about how to avoid reloading the model multiple times to save time.

Loading the model globally outside functions ensures it loads once when the app starts and is reused for all requests, improving performance.

❓ Hyperparameter

advanced

Which FastAPI setting improves concurrency for model serving?

You want to serve a machine learning model with FastAPI and handle many requests at the same time efficiently.

Which Uvicorn server option helps improve concurrency?

A Use <code>--workers 4</code> to run multiple worker processes.

B Use <code>--log-level critical</code> to reduce logging overhead.

C Use <code>--reload</code> to automatically reload on code changes.

D Use <code>--workers 1</code> to keep a single process for simplicity.

💡 Hint

More workers mean more processes to handle requests concurrently.

Using multiple workers (--workers 4) runs several processes to handle requests in parallel, improving concurrency and throughput.

🔧 Debug

advanced

Why does this FastAPI model serving code raise an error?

Examine this FastAPI code snippet:

from fastapi import FastAPI
import pickle

app = FastAPI()

model = pickle.load(open('model.pkl', 'rb'))

@app.post('/predict')
async def predict(data: dict):
    features = data['features']
    prediction = model.predict([features])
    return {'prediction': prediction}

When sending a POST request with JSON {"features": [1, 2, 3]}, it raises a TypeError. Why?

ML Python

from fastapi import FastAPI
import pickle

app = FastAPI()

model = pickle.load(open('model.pkl', 'rb'))

@app.post('/predict')
async def predict(data: dict):
    features = data['features']
    prediction = model.predict([features])
    return {'prediction': prediction}

A FastAPI cannot accept POST requests with JSON bodies.

B The 'features' key is missing in the input JSON.

C pickle.load cannot load the model outside a function.

D The model.predict returns a numpy array which is not JSON serializable.

💡 Hint

Think about what type model.predict returns and how FastAPI converts it to JSON.

The model.predict method returns a numpy array, which FastAPI cannot convert to JSON directly, causing a TypeError. You need to convert it to a list first.

🧠 Conceptual

expert

What is the main advantage of using FastAPI for model serving over Flask?

Choose the best reason why FastAPI is often preferred for serving machine learning models compared to Flask.

A FastAPI requires less code to write HTML templates for web pages.

B Flask does not support JSON responses by default.

C FastAPI automatically generates interactive API docs and supports async code for better performance.

D Flask cannot be deployed on cloud platforms.

💡 Hint

Think about developer experience and performance features.

FastAPI provides automatic interactive API documentation and supports asynchronous programming, which helps handle many requests efficiently, making it ideal for model serving.

Practice

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

easy

A. To create a web service that allows users to send data and get model predictions

B. To train machine learning models faster on GPUs

C. To visualize data with interactive charts

D. To store large datasets efficiently

Solution

1. Step 1: Understand FastAPI's role

   FastAPI is a web framework used to build APIs that can serve machine learning models.

2. Step 2: Identify the main use in ML serving

   It allows sending input data and receiving predictions via HTTP requests, making models accessible.

3. Final Answer:

   To create a web service that allows users to send data and get model predictions -> Option A

4. Quick Check:

   FastAPI serves ML models as web APIs = D [OK]

Hint: FastAPI is for serving models, not training or visualization [OK]

Common Mistakes:

• Confusing FastAPI with training libraries
• Thinking FastAPI is for data storage
• Assuming FastAPI is for data visualization

2. Which of the following is the correct way to define a POST endpoint in FastAPI for model prediction?

easy

A. app.post('/predict')

B. app.put('/predict')

C. app.get('/predict')

D. app.delete('/predict')

Solution

1. Step 1: Recall HTTP methods for sending data

   POST is used to send data to the server, suitable for prediction requests.

2. Step 2: Match method to FastAPI syntax

   FastAPI uses @app.post decorator to define POST endpoints.

3. Final Answer:

   app.post('/predict') -> Option A

4. Quick Check:

   POST endpoint for sending data = app.post [OK]

Hint: Use POST to send data for predictions in FastAPI [OK]

Common Mistakes:

• Using GET instead of POST for sending input data
• Confusing HTTP methods like PUT or DELETE
• Missing the @ decorator syntax

3. Given the following FastAPI code snippet, what will be the output when sending JSON {"text": "hello"} to /predict?

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class InputData(BaseModel):
    text: str

@app.post('/predict')
def predict(data: InputData):
    return {"length": len(data.text)}

medium

A. {"text": 5}

B. {"length": "hello"}

C. {"length": 5}

D. Error: Missing return type

Solution

1. Step 1: Understand input and output

   The input JSON has "text": "hello". The function returns the length of this string.

2. Step 2: Calculate length of 'hello'

   "hello" has 5 characters, so the output JSON is {"length": 5}.

3. Final Answer:

   {"length": 5} -> Option C

4. Quick Check:

   len('hello') = 5 [OK]

Hint: Count characters in input string for length output [OK]

Common Mistakes:

• Returning the string instead of its length
• Confusing keys in the output JSON
• Expecting a syntax or runtime error

4. Identify the error in this FastAPI model serving code:

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class InputData(BaseModel):
    value: int

@app.post('/predict')
def predict(data: InputData):
    return {"result": data.value * 2}

# Client sends JSON: {"value": "10"}

medium

A. Missing return statement

B. Type error because 'value' should be int but client sent string

C. Syntax error in function definition

D. No error, code works fine

Solution

1. Step 1: Check data type in Pydantic model

   InputData expects 'value' as int, client sends string "10".

2. Step 2: Understand Pydantic validation and coercion

   Pydantic automatically converts compatible strings like "10" to int 10, so no validation error occurs.

3. Final Answer:

   No error, code works fine -> Option D

4. Quick Check:

   Pydantic coerces "10" to 10 [OK]

Hint: Pydantic automatically converts compatible types like str to int [OK]

Common Mistakes:

• Thinking Pydantic doesn't auto-convert string to int
• Expecting a type validation error
• Overlooking Pydantic's coercion feature

5. You want to serve a sentiment analysis model using FastAPI. The model expects a list of texts and returns a list of sentiment scores. Which is the best way to define the input and output in FastAPI?

hard

A. Accept raw JSON string and return a plain string

B. Use a Pydantic model with a field 'texts' as List[str] and return List[float]

C. Use query parameters for each text input and return a single float

D. Use a Pydantic model with a single string field and return a dictionary with one score

Solution

1. Step 1: Define input for multiple texts

   Since the model expects multiple texts, input should be a list of strings, best defined with Pydantic List[str].

2. Step 2: Define output for multiple scores

   The model returns multiple sentiment scores, so output should be a list of floats matching input size.

3. Final Answer:

   Use a Pydantic model with a field 'texts' as List[str] and return List[float] -> Option B

4. Quick Check:

   Input/output types match model expectations [OK]

Hint: Match input/output types to model's batch processing [OK]

Common Mistakes:

• Using single string input for multiple texts
• Returning single score instead of list
• Using query params for large input lists