FastAPI Interview Questions and Answers: Part 2 — Validation, Async & Dependency Injection

After understanding the basic FastAPI concepts, the next step is to understand how FastAPI handles data, dependencies, and multiple requests.

This part focuses on concepts that are commonly asked after the basic questions.

1. Explain the difference between ASGI and WSGI. Why does FastAPI use ASGI?

Before understanding FastAPI, we need to understand what ASGI and WSGI actually do.

Think of a web server as a person standing between the browser and your Python application.

The server receives a request and needs a standard way to communicate with the Python application.

That standard communication layer is where WSGI and ASGI come in.

WSGI

WSGI = Web Server Gateway Interface

It was designed mainly for traditional synchronous Python web applications.

For example:

Browser

↓

Web Server

↓

WSGI

↓

Python Application

ASGI

ASGI = Asynchronous Server Gateway Interface

ASGI supports both normal HTTP requests and asynchronous communication.

It is especially useful for applications that need:

Asynchronous operations
Many concurrent requests
WebSockets
Long-lived connections

FastAPI is built for ASGI.

A common setup is:

Client

↓

Uvicorn

↓

ASGI

↓

FastAPI

So why does FastAPI use ASGI?

Because FastAPI is designed to support asynchronous programming and modern communication patterns such as WebSockets.

Simple way to remember

WSGI → Traditional synchronous Python web applications
ASGI → Asynchronous and modern Python web applications

ASGI is one of the foundations that allows FastAPI to support asynchronous request handling.

2. Explain async and await in FastAPI. When should you use async def vs def?

This is one of the most important FastAPI interview questions.

Suppose your application asks another server for some information.

Your application has to wait for the other server to respond.

Instead of doing nothing during that waiting time, asynchronous programming allows the application to work on other tasks.

That's the basic idea behind async and await.

`async def`

We use async def when we want to create an asynchronous function.

@app.get("/users")

async def get_users():

users = await fetch_users()

return users

Here:

await fetch_users()

means:

"I need to wait for this operation, but while waiting, other work can continue."

What about `def`?

A normal function can simply be written as:

@app.get("/health")

def health_check():

return {"status": "OK"}

This is useful when the operation is simple and doesn't need asynchronous waiting.

Important interview point

Don't say:

"async always makes FastAPI faster."

That's not correct.

async is particularly useful for I/O-bound operations, such as:

Database calls

API calls

Network operations

Other asynchronous I/O

It does not magically make CPU-heavy work faster.

Easy example

Imagine one person has to:

Order food

↓

Wait 20 minutes

If they simply stand there doing nothing, that's inefficient.

With asynchronous thinking:

Order food

↓

While food is cooking

↓

Do another task

That is the basic idea behind asynchronous I/O.

3. What are the benefits of using asynchronous programming in FastAPI?

The biggest benefit is better handling of waiting time.

Imagine 100 users send requests to your API.

Many of those requests might be waiting for:

Database

External API

Network

File operation

If the application can handle those waits asynchronously, it can work on other requests instead of unnecessarily sitting idle.

Main benefits

1. Better concurrency

The server can handle multiple I/O-bound operations efficiently.

2. Non-blocking I/O

While one operation is waiting, other work can continue.

3. Better resource utilization

The server doesn't need to waste its time doing nothing while waiting for an external operation.

4. Useful for real-time applications

FastAPI's asynchronous capabilities are also useful when building applications involving WebSockets and other long-running connections.

But remember

Async programming is not a magic performance button.

If your code is doing heavy CPU calculations:

for i in range(100000000):

...

making the function async does not automatically make that calculation faster.

The real benefit of async comes mainly from I/O-bound work.

4. What is Dependency Injection in FastAPI, and how does it work?

This sounds complicated, but the idea is actually simple.

Suppose you have 20 API endpoints.

All 20 endpoints need a database connection.

You could write database connection code inside every endpoint.

That would create a lot of repeated code.

Instead, create the database logic once and ask FastAPI to provide it whenever an endpoint needs it.

This is Dependency Injection.

FastAPI provides this using:

Depends()

Example:

from fastapi import Depends

def get_database():

return "Database Connection"

@app.get("/users")

def get_users(db = Depends(get_database)):

return {"message": "Using database"}

Here:

Depends(get_database)

tells FastAPI:

"Before running get_users(), call get_database() and give its result to db."

So the endpoint doesn't need to create the dependency itself.

Simple real-life example

Imagine you go to school.

You need a notebook.

Instead of going to the shop every time you need one, someone responsible gives you the notebook when you need it.

That's roughly the idea of Dependency Injection:

The function says what it needs, and FastAPI provides it.

5. How does dependency injection simplify code organization and testing in FastAPI?

Without Dependency Injection, imagine this:

Endpoint 1 → creates database

Endpoint 2 → creates database

Endpoint 3 → creates database

Endpoint 4 → creates database

The same logic is repeated everywhere.

With Dependency Injection:

get_db()

↓

┌────────┼────────┐

↓ ↓ ↓

Users Orders Products

All endpoints can reuse the same dependency.

This gives us three major benefits.

Code reuse

Write common logic once and use it in many places.

Separation of responsibility

The endpoint focuses on what it needs to do instead of worrying about how every dependency is created.

Easy testing

During testing, we can replace the real dependency with a fake or test version.

For example, instead of connecting to the real production database during a test, we can provide a test database.

This is one of the reasons Dependency Injection is important in larger applications.

6. Provide an example of using dependency injection in FastAPI.

Let's create a simple dependency.

from fastapi import FastAPI, Depends

app = FastAPI()

def common_parameters():

return {

"limit": 10

}

@app.get("/items")

def get_items(params = Depends(common_parameters)):

return {

"parameters": params

}

When a client calls:

GET /items

FastAPI sees:

Depends(common_parameters)

So it executes:

common_parameters()

The returned value is given to:

params

The response becomes:

{

"parameters": {

"limit": 10

}

What happened?

Request

↓

FastAPI

↓

common_parameters()

↓

Result

↓

get_items(params)

↓

Response

The same idea can be used for much more important things such as:

Database sessions

Authentication

Current user

Authorization

Configuration

Reusable parameters

FastAPI's Depends() is specifically designed to declare these dependencies.

7. How do you define request body using Pydantic models?

When a client sends data to an API, that data often comes in the request body.

For example, suppose we want to create a product.

The frontend might send:

{

"name": "Laptop",

"price": 50000,

"in_stock": true

}

We can define the expected structure using Pydantic.

from pydantic import BaseModel

class Product(BaseModel):

price: float

in_stock: bool

Then use it in our endpoint:

@app.post("/products")

def create_product(product: Product):

return product

Now FastAPI knows that the request should contain:

name → string

price → number

in_stock → boolean

If the client sends incorrect data, FastAPI can reject it during validation.

Why is this useful?

Without a model, we would have to manually check every field.

Pydantic gives us a clean way to define:

"This is the shape of data my API expects."

8. What is the purpose of response_model in FastAPI path operations?

We have seen how Pydantic can validate incoming data.

But what about data going out of the API?

That's where response_model becomes useful.

Suppose our database contains:

name

password_hash

We don't want to accidentally send the password hash to the frontend.

We can define:

from pydantic import BaseModel

class UserResponse(BaseModel):

id: int

email: str

Then:

@app.get("/users/{user_id}", response_model=UserResponse)

def get_user(user_id: int):

return {

"id": 1,

"name": "Vijay",

"email": "[email protected]",

"password_hash": "secret-value"

}

The response model defines what the API should return.

Conceptually:

Database

↓

Full User Object

↓

response_model

↓

Only allowed fields

↓

Frontend

So response_model helps with:

Response validation
Consistent response structure
API documentation
Avoiding accidental exposure of fields

9. How can you validate a specific field in a Pydantic model?

Sometimes checking the data type isn't enough.

For example:

price = -500

The value is technically a number.

But our business rule might say:

Price cannot be negative.

So we need custom validation.

A Pydantic model can contain validation rules.

For example:

from pydantic import BaseModel, Field

class Product(BaseModel):

price: float = Field(gt=0)

Now the price must be greater than zero.

The important idea is:

Data type validation

Business rules

↓

Valid input

For more complex rules, Pydantic provides validator mechanisms.

For example, we might need rules such as:

Password must be at least 8 characters

Age must be above 18

Start date cannot be after end date

This is where custom validation becomes useful.

10. What is APIRouter and how to structure a large FastAPI application?

When we start learning FastAPI, keeping everything inside main.py is fine.

For example:

main.py

might contain:

Users

Products

Orders

Payments

Authentication

But imagine having 500 endpoints in one file.

It would become difficult to understand and maintain.

That's why FastAPI provides APIRouter.

We can separate routes:

app/

│

├── main.py

│

└── routers/

├── users.py

├── products.py

├── orders.py

└── auth.py

For example, in users.py:

from fastapi import APIRouter

router = APIRouter()

@router.get("/users")

def get_users():

return {"users": []}

Then in main.py:

from fastapi import FastAPI

from routers.users import router as user_router

app = FastAPI()

app.include_router(user_router)

Now the application is divided into smaller pieces.

Think of it like a school

Instead of keeping every subject in one notebook:

One giant notebook

we use:

Maths

Science

English

Computer Science

Each section has its own place.

APIRouter provides a similar organizational benefit for API routes.

It helps with:

Cleaner code
Better organization
Easier maintenance
Team development
Separating features into modules

APIRouter is particularly useful as a FastAPI application grows larger.

The Bigger Picture

After Part 1, we understood:

FastAPI

Routes

HTTP methods

Path parameters

Query parameters

OpenAPI

Pydantic basics

Now in Part 2, we have moved one level deeper:

FastAPI

↓

┌────────┴────────┐

↓ ↓

Request Response

↓ ↓

Pydantic response_model

↓

Validation

↓

Dependency

Injection

↓

Depends()

↓

APIRouter

And behind all of this:

FastAPI

↓

Starlette

↓

ASGI

↓

Uvicorn

What comes next?

The next level is where FastAPI interviews start getting much more practical:

WebSockets → Background Tasks → Middleware → CORS → Exception Handling → Testing → Database → JWT/OAuth2 → Security.

Those are better kept for Part 3 and Part 4, rather than making Part 2 unnecessarily huge. The source question sets place these topics in the more advanced/common sections as well.

My review: This Part 2 is the right jump from Blog 1. It doesn't simply repeat “What is FastAPI?” or basic GET/POST questions, but makes the reader understand how FastAP

actually handles data and dependencies. That's exactly the level we want before moving into security and production topics.

FastAPI Interview Questions and Answers: Part 2 — Validation, Async & Dependency Injection

FastAPI Interview Questions and Answers: Part 2 — Validation, Async & Dependency Injection

1. Explain the difference between ASGI and WSGI. Why does FastAPI use ASGI?

WSGI

ASGI

Simple way to remember

2. Explain async and await in FastAPI. When should you use async def vs def?

`async def`

What about `def`?

Important interview point

Easy example

3. What are the benefits of using asynchronous programming in FastAPI?

Main benefits

1. Better concurrency

2. Non-blocking I/O

3. Better resource utilization

4. Useful for real-time applications

But remember

4. What is Dependency Injection in FastAPI, and how does it work?

Simple real-life example

5. How does dependency injection simplify code organization and testing in FastAPI?

This gives us three major benefits.

6. Provide an example of using dependency injection in FastAPI.

What happened?

7. How do you define request body using Pydantic models?

Why is this useful?

8. What is the purpose of response_model in FastAPI path operations?

9. How can you validate a specific field in a Pydantic model?

10. What is APIRouter and how to structure a large FastAPI application?

Think of it like a school

The Bigger Picture

What comes next?

Comments

Join Techsnap Creators

FastAPI Interview Questions and Answers: Part 2 — Validation, Async & Dependency Injection

FastAPI Interview Questions and Answers: Part 2 — Validation, Async & Dependency Injection

1. Explain the difference between ASGI and WSGI. Why does FastAPI use ASGI?

WSGI

ASGI

Simple way to remember

2. Explain async and await in FastAPI. When should you use async def vs def?

async def

What about def?

Important interview point

Easy example

3. What are the benefits of using asynchronous programming in FastAPI?

Main benefits

1. Better concurrency

2. Non-blocking I/O

3. Better resource utilization

4. Useful for real-time applications

But remember

4. What is Dependency Injection in FastAPI, and how does it work?

Simple real-life example

5. How does dependency injection simplify code organization and testing in FastAPI?

This gives us three major benefits.

6. Provide an example of using dependency injection in FastAPI.

What happened?

7. How do you define request body using Pydantic models?

Why is this useful?

8. What is the purpose of response_model in FastAPI path operations?

9. How can you validate a specific field in a Pydantic model?

10. What is APIRouter and how to structure a large FastAPI application?

Think of it like a school

The Bigger Picture

What comes next?

Comments

Join Techsnap Creators

`async def`

What about `def`?