Top 20 FastAPI Interview Questions and Answers (2026 Guide)

Over the past five years, FastAPI has rapidly become the dominant Python framework for building high-performance microservices, machine learning model APIs, and data backend services. Built on top of Starlette and Pydantic, it combines the developer ergonomics of Python with execution performance rivaling Go and Node.js.

When technical interviewers evaluate candidates on fastapi interview questions, they test more than just route syntax. They want to know: Do you understand when an async endpoint blocks the event loop? How do you safely manage database session lifecycles with yield? How do you filter sensitive attributes using response_model?

In this comprehensive technical guide, we break down the top 20 fastapi interview questions, complete with production code snippets, architectural comparisons against Flask and Django, and real-world best practices.

To review foundational networking and Python skills, explore our REST API Interview Questions, Python Basic Interview Questions, and comprehensive Python tutorial.

Top 20 FastAPI Interview Questions and Answers

Q1: What is FastAPI and why is it so fast?

Answer: FastAPI is a modern, high-performance web framework for building APIs with Python 3.8+ based on standard Python type hints. Its speed comes from two foundational pillars:

1. Starlette: Handles high-performance routing and asynchronous networking over ASGI (Asynchronous Server Gateway Interface).
2. Pydantic: Handles data validation, serialization, and schema generation. In Pydantic v2, core parsing algorithms are compiled directly in Rust, providing up to 20x faster data validation than pure Python.
3. Native Concurrency: Built from the ground up to support Python's asyncio event loop.

Q2: What is the architectural difference between ASGI and WSGI?

Answer:

• WSGI (Web Server Gateway Interface - Flask, Django): A synchronous standard. A WSGI server worker handles one HTTP request at a time and blocks while waiting for I/O operations (like database queries or external API calls) to finish before picking up the next request.
• ASGI (Asynchronous Server Gateway Interface - FastAPI, Starlette): An asynchronous standard. An ASGI server (like Uvicorn) runs an event loop. When a request awaits I/O, the worker pauses that task and handles other incoming requests concurrently on the same thread, delivering massive throughput under high I/O concurrency.

Q3: When should you define an endpoint with async def vs regular def in FastAPI?

Answer: This is one of the most critical conceptual questions in FastAPI interviews:

• async def: Use when the code inside your endpoint calls non-blocking libraries using await (e.g. await database.fetch_all(), await httpx_client.get()). Danger: If you write async def and execute blocking synchronous code (like time.sleep(5) or synchronous requests.get()), you freeze the entire main event loop, stalling all other concurrent requests for the entire server!
• Regular def: Use when executing traditional synchronous, blocking code (e.g. synchronous SQLAlchemy, pandas data manipulation, CPU-bound computations). FastAPI automatically detects standard def and offloads the function to an external threadpool (anyio), preventing the main event loop from blocking.

# GOOD: Non-blocking I/O
@app.get("/items-async")
async def read_async():
    data = await async_db_query()
    return data
 
# GOOD: Blocking I/O offloaded to threadpool automatically
@app.get("/items-sync")
def read_sync():
    time.sleep(2)  # Safely runs in threadpool; does NOT block event loop
    return {"status": "ok"}

Q4: How does Pydantic integrate with FastAPI for data validation?

Answer: FastAPI uses Pydantic BaseModel schemas for request body parsing, type coercion, and schema documentation:

1. Validation: Validates incoming JSON against declared types (integers, strings, UUIDs, datetimes).
2. Coercion: Automatically casts matching types (e.g., string "123" into integer 123).
3. Error Reporting: Automatically returns structured 422 Unprocessable Entity responses detailing exactly which field failed validation.

from pydantic import BaseModel, EmailStr, Field
 
class UserCreate(BaseModel):
    name: str = Field(..., min_length=2, max_length=50)
    email: EmailStr
    age: int = Field(gt=0, le=120)
 
@app.post("/users")
def create_user(user: UserCreate):
    return {"message": f"User {user.name} validated!"}

Q5: How does Dependency Injection (Depends) work in FastAPI?

Answer: FastAPI features a hierarchical Dependency Injection system powered by Depends. Dependencies are reusable callables (functions or classes) that run before your route handler.

Key benefits:

• Code reuse (shared database sessions, auth checks, pagination params).
• Decoupled testing (trivially override dependencies in unit tests using app.dependency_overrides).
• Automatic cleanup using yield generators.

from fastapi import Depends
 
def get_db():
    db = DatabaseSession()
    try:
        yield db  # Injected into route
    finally:
        db.close()  # Automatically executed after response finishes
 
@app.get("/orders")
def get_orders(db: DatabaseSession = Depends(get_db)):
    return db.query_orders()

Q6: FastAPI vs Flask vs Django: When should you choose which?

Answer:

Q7: What are Path, Query, and Body parameters and how are they declared?

Answer:

• Path Parameter: Declared directly in the route URL path:

  python

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  @app.get("/users/{user_id}")
  def get_user(user_id: int): ...

• Query Parameter: Any function parameter not matching a path parameter and of a primitive type (int, str, bool):

  python

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  @app.get("/items")
  def list_items(skip: int = 0, limit: int = 20): ...
  # Invoked as: /items?skip=0&limit=20

• Request Body: Parameters declared as Pydantic models:

  python

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  @app.post("/items")
  def create_item(item: ItemSchema): ...

Q8: How do Background Tasks work in FastAPI?

Answer: FastAPI provides a BackgroundTasks class to execute lightweight tasks asynchronously after returning the HTTP response to the client (e.g. sending confirmation emails or writing audit logs):

from fastapi import BackgroundTasks
 
def send_welcome_email(email: str):
    # Simulated email service
    time.sleep(3)
 
@app.post("/register")
def register_user(email: str, background_tasks: BackgroundTasks):
    background_tasks.add_task(send_welcome_email, email)
    return {"message": "Account created. Confirmation email sent in background."}

Note: For intensive, multi-hour background jobs, use a distributed task queue like Celery or RQ backed by Redis.

Q9: How do you implement OAuth2 with JWT in FastAPI?

Answer:

1. Use fastapi.security.OAuth2PasswordBearer to declare the token endpoint.
2. In the token route, verify username and password, encode claims into a JWT signed with a secret key, and return the token.
3. In protected endpoints, declare a dependency that decodes the JWT from the Authorization: Bearer <token> header:

from fastapi.security import OAuth2PasswordBearer
from fastapi import Depends, HTTPException
 
oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
 
def get_current_user(token: str = Depends(oauth2_scheme)):
    payload = decode_jwt(token)
    if not payload:
        raise HTTPException(status_code=401, detail="Invalid token")
    return payload["user_id"]
 
@app.get("/protected-profile")
def view_profile(user_id: int = Depends(get_current_user)):
    return {"user_id": user_id}

Q10: How do you filter sensitive data using response_model?

Answer: To prevent accidental data leaks (such as returning password hashes stored in database objects), specify a public response_model. FastAPI filters out any fields not present in the response schema:

class UserDB(BaseModel):
    id: int
    name: str
    email: str
    hashed_password: str
 
class UserPublic(BaseModel):
    id: int
    name: str
    email: str
 
@app.get("/users/{user_id}", response_model=UserPublic)
def get_user(user_id: int):
    user = db.get_user(user_id) # Contains hashed_password
    return user # FastAPI automatically strips hashed_password!

Q11: How do you configure CORS in FastAPI?

Answer: Add the CORSMiddleware with explicitly configured allowed origins:

from fastapi.middleware.cors import CORSMiddleware
 
app.add_middleware(
    CORSMiddleware,
    allow_origins=["https://dashboard.topfolio.in"],
    allow_credentials=True,
    allow_methods=["GET", "POST", "PUT", "DELETE"],
    allow_headers=["*"],
)

Q12: How do you handle custom exceptions with HTTPException?

Answer: Raise fastapi.HTTPException with a status code and detail payload:

from fastapi import HTTPException
 
@app.get("/items/{item_id}")
def read_item(item_id: int):
    item = db.find(item_id)
    if not item:
        raise HTTPException(status_code=404, detail="Item not found")
    return item

Q13: What are Lifespan Events in modern FastAPI?

Answer: Modern FastAPI replaces deprecated @app.on_event("startup") with the @asynccontextmanager lifespan protocol. It cleanly separates startup and shutdown logic around a yield:

from contextlib import asynccontextmanager
from fastapi import FastAPI
 
@asynccontextmanager
async def lifespan(app: FastAPI):
    # Startup: Initialize database pool, warm up ML models
    db_pool = await create_db_pool()
    yield
    # Shutdown: Clean up connections
    await db_pool.close()
 
app = FastAPI(lifespan=lifespan)

Q14: How do you organize a large production FastAPI application?

Answer: Use APIRouter to split routes across functional domain files:

app/
├── main.py              # App instantiation, middleware, router inclusions
├── core/config.py       # Pydantic BaseSettings environment variables
├── api/
│   ├── v1/
│   │   ├── auth.py      # APIRouter for auth
│   │   ├── users.py     # APIRouter for users
│   │   └── analytics.py # APIRouter for metrics
├── models/              # SQLAlchemy / SQLModel database entities
└── schemas/             # Pydantic validation schemas

In main.py:

app.include_router(auth.router, prefix="/api/v1/auth", tags=["auth"])

Q15: How do you test FastAPI endpoints using pytest and httpx?

Answer: Use TestClient (built on httpx) to write fast, synchronous unit tests:

from fastapi.testclient import TestClient
from app.main import app
 
client = TestClient(app)
 
def test_health_check():
    response = client.get("/health")
    assert response.status_code == 200
    assert response.json() == {"status": "healthy"}

Q16: What is the difference between UploadFile and bytes in file uploads?

Answer:

• bytes: Reads the entire payload into server memory. If a user uploads a 2GB file, server RAM spikes by 2GB, causing Out-Of-Memory (OOM) crashes under concurrency.
• UploadFile: Streams data as a SpooledTemporaryFile. It stores data in memory up to a rollover limit (1MB), then streams overflow to a temporary disk buffer. Supports await file.read(chunk_size) safely.

Q17: How does automatic interactive API documentation work in FastAPI?

Answer: FastAPI automatically parses route definitions, type annotations, and Pydantic schemas into an OpenAPI standard JSON document at /openapi.json. It serves two interactive browser user interfaces:

• Swagger UI at /docs: Allows real-time in-browser testing with authorization token injection.
• ReDoc at /redoc: A clean, publication-ready API reference layout.

Q18: What is app.dependency_overrides and why is it essential in testing?

Answer: In unit tests, you should never hit production databases or external third-party payment gateways. app.dependency_overrides allows you to swap out any Depends callable with a mock fixture:

def mock_get_db():
    return FakeTestDatabase()
 
app.dependency_overrides[get_db] = mock_get_db

Q19: How do you handle configuration using Pydantic Settings?

Answer: Use pydantic-settings to load and validate environment variables with strict type checking:

from pydantic_settings import BaseSettings
 
class Settings(BaseSettings):
    database_url: str
    jwt_secret: str
    debug: bool = False
 
    class Config:
        env_file = ".env"
 
settings = Settings()

Q20: How do you run and deploy FastAPI in production?

Answer: Never run uvicorn main:app --reload in production. Production deployment patterns include:

1. Containerized Deployment (Docker + Uvicorn) (for deploying containerized microservices into orchestrated clusters, see our guide on Kubernetes Interview Questions):

   bash

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   uvicorn app.main:app --host 0.0.0.0 --port 8000 --workers 4

2. Gunicorn with Uvicorn Worker Class: Uses Gunicorn as the master process manager to restart crashed workers, delegating async execution to Uvicorn workers:

   bash

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   gunicorn -w 4 -k uvicorn.workers.UvicornWorker app.main:app

3. Reverse Proxy (Nginx / Cloudflare): Fronts the ASGI workers to terminate SSL, serve static assets, and buffer slow client requests.

FastAPI Interview Questions by Level

How to Prepare for FastAPI Technical Interviews

1. Understand Asynchronous Python Internals: Be ready to explain how asyncio, the event loop, and coroutines function.
2. Build a Microservice with Authentication: Practice writing a full JWT authentication flow with hashed passwords and route protection.
3. Review RESTful Design Principles: Ensure your endpoint verbs and HTTP status codes follow standard REST semantics as detailed in our REST API Interview Questions.

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