FastAPI Interview Questions for Indian Backend Roles (2026)
TL;DR
The FastAPI questions that actually come up in Indian backend interviews in 2026 — async vs sync, dependency injection, Pydantic validation, background tasks, and the production-depth follow-ups that separate juniors from mid-level hires. With model answers.
FastAPI has become the default Python framework in Indian backend interviews — most product companies and startups hiring for Python roles now expect it. But the questions have moved past "what is FastAPI." Interviewers in 2026 probe whether you understand the async model, dependency injection, and production concerns — because that's what separates someone who followed a tutorial from someone who has shipped.
Here are the questions that actually come up, grouped by depth, with the kind of answers that land offers. For framework context, see FastAPI vs Django; for the broader interview, the system design questions for India.
Level 1: The warm-ups
These filter out people who've never touched it. Answer crisply and move on.
Q: What is FastAPI and why is it fast? A modern Python web framework built on Starlette (ASGI) and Pydantic. It's fast for two reasons: it's async-native (ASGI lets it handle many concurrent I/O-bound requests without blocking), and Pydantic does validation in highly optimized code. The third "fast" is developer speed — automatic OpenAPI docs and type-based validation.
Q: How does FastAPI generate API docs automatically?
From your type hints and Pydantic models, it builds an OpenAPI schema, served as interactive Swagger UI (/docs) and ReDoc (/redoc). The same type hints drive validation and documentation — define once, get both.
Q: What's a Pydantic model and why use one? A class that declares the shape and types of data. FastAPI uses it to validate and parse request bodies and serialize responses. Invalid input is rejected with a structured 422 before your handler runs — you never hand-write validation boilerplate.
Level 2: The async model (where most candidates stumble)
This is the single most important area, and the most common place people get exposed.
Q: What's the difference between def and async def for a route?
This is the FastAPI gotcha. A route defined with async def runs on the event loop — great for await-ing I/O (DB, HTTP calls). A route defined with plain def is run in a threadpool so it doesn't block the loop. The trap:
If you writeasync defand then call a blocking function inside it (a synchronous DB driver,time.sleep,requests.get), you block the entire event loop — every concurrent request stalls.
# BAD: blocking call inside async route — freezes the event loop
@app.get("/users")
async def get_users():
return requests.get("https://api.example.com/users").json() # blocks!
# GOOD: await an async client
@app.get("/users")
async def get_users():
async with httpx.AsyncClient() as client:
r = await client.get("https://api.example.com/users")
return r.json()Strong answer: "Use async def only when everything inside is awaitable. If I must call a blocking library, either use a sync def route (FastAPI threadpools it) or push the work off the loop with run_in_executor." Saying this out loud signals real experience.
Q: How do you run a blocking/CPU-bound task without killing throughput?
Three options, by case: a plain def route (auto-threadpooled), await loop.run_in_executor(...) for a blocking call inside an async route, or — for genuinely heavy CPU work — offload to a background worker (Celery, Dramatiq, or an external queue) rather than doing it in the request at all.
Level 3: Dependency injection
DI is FastAPI's signature feature and a favourite interview topic.
Q: How does FastAPI's Depends work?
You declare a dependency as a function; FastAPI calls it and injects its result into your handler. Dependencies can have their own dependencies (a tree), are resolved per-request, and are cached within a request. It's how you wire DB sessions, auth, and shared config cleanly.
async def get_db():
async with SessionLocal() as session:
yield session # cleanup runs after the response
async def get_current_user(token: str = Depends(oauth2_scheme),
db = Depends(get_db)):
user = await verify_token(db, token)
if not user:
raise HTTPException(401, "Invalid credentials")
return user
@app.get("/me")
async def me(user = Depends(get_current_user)):
return userQ: Why use yield in a dependency?
Code before yield runs on the way in (acquire a DB session); code after yield runs on the way out (close it) — even if the handler raised. It's FastAPI's clean equivalent of setup/teardown, perfect for resource lifecycle like DB connections — which ties straight into connection pooling.
Level 4: Production depth (the mid-level differentiators)
These follow-ups are where the mid-level offer is won. They're rarely about FastAPI syntax and mostly about whether you've operated a service.
Q: A FastAPI endpoint is slow under load. How do you debug it? Walk the layers out loud: confirm with p95/p99 metrics (not averages); check whether a blocking call is stalling the event loop; inspect the database (slow query? pool exhausted? — see connection pooling); look for an N+1 query; check whether a cache stampede hits the DB at TTL boundaries. Structured reasoning beats a single guessed answer.
Q: How do you handle background work — say, sending an email after signup?
For light, fire-and-forget work, FastAPI's BackgroundTasks runs it after the response is sent. For anything that can fail, retry, or is heavy, use a real task queue (Celery/Dramatiq) so the work is durable and the web process stays responsive. Knowing when BackgroundTasks is not enough is the senior signal.
Q: How do you make a POST safe to retry?
Idempotency keys — accept a client-generated key, claim it atomically, replay the stored result on duplicates. This is a great moment to reference real experience; I wrote it up in idempotency keys: how we stopped double-charging customers.
Q: How do you structure a large FastAPI project?
Routers per domain (APIRouter), a settings module (Pydantic BaseSettings), dependencies for DB/auth, a service layer separating business logic from HTTP, and Alembic for migrations. Emphasize separation of concerns — handlers thin, logic in services.
Q: Sync framework (Django/Flask) vs FastAPI — when would you not pick FastAPI? Maturity signal. FastAPI shines for async, I/O-bound, API-first services. Django still wins when you want batteries-included (admin, ORM, auth) and a large team moving fast on a CRUD-heavy product. The honest "it depends" with reasons beats fanboying — expanded in FastAPI vs Django.
How to actually prepare
- Build one real async service end to end — routes, DI, Pydantic models, an async DB driver, auth, and background tasks. Interviewers smell tutorial-only knowledge instantly.
- Be able to explain the event loop in your own words. The
async def+ blocking-call trap is asked constantly; nail it. - Connect FastAPI to production stories — a slow endpoint you fixed, a retry bug you solved, a pool you tuned. Specifics win offers.
- Practice the "it depends" answers with real trade-offs, not opinions.
The takeaway
In 2026, FastAPI interviews for Indian backend roles reward systems understanding over syntax. Know the async model cold, explain dependency injection cleanly, and back every production question with something you've actually debugged. Do that, and you'll clear the bar that filters out the tutorial crowd — and land in the higher salary bands.
Related reading: