Top FastAPI Interview Questions to Ace Your 2025 Job Search

FastAPI has become a favorite for building APIs in Python, and it’s popping up everywhere in job listings. If you’re looking to land a tech job in 2025, you’ll probably run into some fastapi interview questions. Whether you’re just starting out or have a few years under your belt, getting familiar with what interviewers might ask can help you feel less nervous and more ready. Here are the top topics and questions you should know before your next interview.

Key Takeaways

• FastAPI is popular because it’s quick to set up and easy to use for building APIs.
• Understanding dependency injection in FastAPI is important for writing reusable and testable code.
• Knowing how to handle path and query parameters will help you answer common API design questions.
• Pydantic models are used for data validation, so expect questions about how they work with FastAPI.
• Being able to discuss error handling, middleware, and deployment will show you’re ready for real-world projects.

1. FastAPI Basics

Starting out with FastAPI, it’s pretty wild how much ground you can cover in such a short time. FastAPI is known for its speed and its simple interface, letting you create APIs quickly without too much fuss. I remember my first project—I had an API up and running in less than an hour, and honestly, it felt a bit too easy after wrestling with older frameworks.

FastAPI is built on top of Starlette for the web parts, and Pydantic for data handling, which means you get a modern design that supports asynchronous code out of the box. That, plus clear syntax, makes it a go-to for RESTful APIs. If speed, code readability, and handy documentation are your priorities, FastAPI really stands out among newer Python web tools. See a bit more about its key philosophy in this high-performance Python web framework summary.

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FastAPI really shines in a few main areas:

• Automatic interactive docs (Swagger UI and ReDoc) let you try your API right in the browser, so no more endless curl commands.
• Type hints aren’t just for show—they actually guide documentation, validation, and even code completion in your editor. You’ll catch lots of bugs before you ever run the code.
• True async support, so you can write modern Python and handle loads of connections without losing your mind over threads.

Here’s a quick comparison with other frameworks, if you like numbers:

Feature	FastAPI	Flask	Django REST
Async support	Yes	Limited	Limited
Type-hint validation	Yes	No	No
Auto Docs	Yes	No	Partial
Performance (requests/sec)	High	Moderate	Moderate

When you’re prepping for interviews, you can count on being asked about these basics: what makes FastAPI tick, where it’s used most, and why you might pick it over something like Flask. Above all, make sure you’re comfortable talking about its core strengths, structure, and a time when its features made your job easier (even if that job was just experimenting on a weekend).

2. Dependency Injection

Dependency injection in FastAPI is one of those features that, once you get the hang of it, you start to wonder how you ever built APIs without it. Basically, it lets you define bits of code (like database connections, authentication checks, or configuration settings) outside your main path operations, and then FastAPI handles passing them in whenever you need them. This means less glue code and more reusability.

Here’s what makes dependency injection super helpful in FastAPI:

• Cleaner route handlers: You don’t clutter your path operations with repetitive code. Instead, you declare what you need with Depends, and FastAPI figures out when and how to provide it.
• Easier testing: Since dependencies are declared up front, you can swap them out for mocks or stubs in your tests. No hidden setup, just plug in what you want.
• Automatic lifecycle management: FastAPI can ensure dependencies are created and destroyed at the right moment—think opening and closing database sessions without you having to remember all the details.

If you’re prepping for a FastAPI interview, it helps to know not just the how, but the why. Here are some patterns you’ll run into:

1. Request-scoped dependencies (e.g., a fresh database session per request).
2. Shared dependencies using @lru_cache() for singletons (like reading config files once).
3. Using dependencies for things other than data—like authentication, authorization, logging, or rate limiting.

One thing that often comes up is the difference between a dependency that’s just a value, versus one that needs to do heavy lifting (maybe it queries a database or reads from disk). FastAPI lets you control that.

Here’s a quick table on dependency scopes you might mention:

Dependency Scope	Use Case Example	Notes
Per-request	DB sessions, user auth	Created for every request
App-wide (cached)	App settings, secrets	Created once per app start

Understand how dependencies can be nested—one dependency can call another, and FastAPI manages the chaining for you. This is great for splitting tasks like validation, database access, or permission checks into small, reusable pieces.

To wrap up: practice writing code where you break out logic into dependencies, inject what you need into your route handlers, and test swapping those dependencies without changing the rest of the code. That’s what interviewers want to see—real, working knowledge, not just repeating what’s in the docs.

3. Path and Query Parameters

Working with path and query parameters is something you’ll always come across using FastAPI, and honestly, it’s way less of a headache than with some other frameworks. In FastAPI, path parameters are the parts of your URL marked by curly braces, like /items/{item_id}. These are used when the data is required by the path (think resource IDs). On the other hand, query parameters come after the ? in your URL, like /items?name=book&count=2, and they’re totally optional.

Here’s what you should know and probably mention in a FastAPI interview:

• Path parameters are defined directly in the route string (e.g., /users/{user_id}), and you can type-check them with a colon (:) and the type—FastAPI will auto-validate them.
• Query parameters are set as function arguments with default values. If you don’t set a default, they become required.
• Both types can be combined in a single endpoint, making your API flexible and clean at the same time.
• FastAPI auto-generates OpenAPI docs, showing you exactly which parameters are required and which are optional, and this makes it easy to see how to interact with each endpoint.

There’s this really interesting way FastAPI simplifies API development compared to other frameworks (kind of like this big leap forward in tech). I’ve found most interviewers want you to write a quick example—don’t panic, just remember:

from fastapi import FastAPI
app = FastAPI()

@app.get("/items/{item_id}")
def read_item(item_id: int, name: str = None):
    return {"item_id": item_id, "name": name}

A typical interview mistake is getting confused about which parameters are required. Quick tip: path params have to be in the URL, query params are optional unless you remove the default value and let FastAPI handle the enforcement.

Quick checklist:

• Know the difference and how they appear in URLs.
• Understand that type validation happens automatically.
• Be ready for a live-coding prompt to show off this stuff.

If you get this down, you’ll look like you actually use the framework—not just memorize theory.

4. Request and Response Models

Working with request and response models is a huge part of FastAPI development. These models act as the bridge between your API and the data it takes in or sends out. In FastAPI, Pydantic models are the workhorse for managing and checking both incoming requests and outgoing responses. If you’re trying to pass around Python dictionaries or JSON everywhere, you’ll quickly miss out on validation, auto-complete, and great docs generation.

Here’s why request and response models actually matter:

• They help make sure the data your API receives is exactly what you expect—nothing more, nothing less.
• Automatic OpenAPI documentation is generated, so consumers of your API know what to send and what they’ll get back.
• If the incoming data doesn’t match the model, FastAPI will send a clear error message back. Less time guessing what went wrong.
• They allow for handy features, like setting field defaults, marking fields as optional, and even checking specific types (think: email, datetime, URLs).

Here’s a quick rundown of the basic flow:

1. You define a Pydantic class with the fields you expect.
2. Use this class as a request body or response model in your endpoint functions.
3. FastAPI parses the incoming data, checks it, and converts it to the correct type. If you’re returning data, it serializes the response based on your model.

A common pattern you’ll see is something like this:

from fastapi import FastAPI
from pydantic import BaseModel

class Item(BaseModel):
    name: str
    price: float
    is_offer: bool = None

app = FastAPI()

@app.post("/items/", response_model=Item)
def create_item(item: Item):
    return item

One little detail: you can have different models for input (request) and output (response), which is great for hiding fields or returning read-only values.

If you want to see how other frameworks stack up for API development, detailed app comparisons give a decent idea of why people pick FastAPI for building APIs quickly and safely.

Bottom line: request and response models keep your API reliable, give you helpful errors, and let you document things with barely any extra work. Once you get used to having them, it’s hard to go back.

5. Data Validation with Pydantic

Working with FastAPI, you’ll run into Pydantic a lot. It’s the go-to tool for defining and validating the data your API handles. Pydantic makes sure any data coming into your endpoints fits strict rules, and if something’s off, you’ll get clear error messages. This isn’t just about checking types—Pydantic checks formats, applies constraints, and rejects extra fields, all while automatically generating API docs for you thanks to its tight integration with FastAPI.

Here’s a quick rundown of how you might use it day-to-day:

• You create classes that inherit from BaseModel to describe what valid data looks like.
• Fields can have type hints, default values, and extra options like min_length, max_length, or regex patterns.
• If someone sends a request that doesn’t fit your model, FastAPI returns a detailed response explaining what went wrong.

Some common things you’ll do with Pydantic models:

1. Set default values for fields so your API doesn’t blow up if data is missing.
2. Add constraints to fields, like making sure age is a positive number.
3. Use nested models to mirror complex JSON structures in your requests and responses.

Typical model example:

from pydantic import BaseModel, Field

class User(BaseModel):
    username: str = Field(..., min_length=3, max_length=20)
    age: int = Field(..., gt=0, lt=120)
    email: str

In this example, FastAPI and Pydantic will block any input that misses these requirements.

Why is this important in interviews? People want to see that you know:

• How to catch bad data before it causes bugs or security problems
• How strong types can make your APIs more reliable and easier to maintain
• How validation and error messages make things nicer for the folks using your API

Bottom line? Understanding Pydantic for data validation is a must-have for anyone serious about FastAPI.

6. Asynchronous Programming

If you’ve ever tried to handle a lot of incoming requests with traditional web frameworks, you probably noticed how things can slow down—waiting for database queries, file reads, or just network calls. FastAPI makes all this feel way easier thanks to its support for asynchronous programming out of the box.

Asynchronous functions (declared with async def) let your API serve more users at once, without getting tied up by slow operations. Basically, the app won’t get stuck waiting for something to finish; it moves on to the next request and circles back when data is ready. This design is great for APIs that talk to other services, deal with slow storage, or process lots of parallel user traffic.

If you’re prepping for interviews, be sure to know how (and when) to use async in FastAPI. Here are key things you should brush up on:

• Use async def for paths that do I/O operations (database, HTTP requests, etc.), so FastAPI can use event loops efficiently.
• Mixing def and async def is fine, but only use async def if you’re performing asynchronous tasks (file, DB, network calls).
• FastAPI uses Starlette under the hood—a framework built for speed using async features.
• Be careful: not all libraries support async! For example, if your database driver is synchronous, using async won’t help much until you swap it for an async-friendly one.

Here’s a quick side-by-side of sync vs async in FastAPI:

Feature	Synchronous (def)	Asynchronous (async def)
Handles many requests well	✗	✓
Blocks app on I/O	✓	✗
Needs async libraries	✗	✓ (for full benefits)

If you want more tips—like how async fits with dependency injection and middleware—here’s an advanced FastAPI interview questions resource that’s worth a look.

In short: mastering async in FastAPI will set you apart, especially with companies wanting snappy, modern APIs for their projects. Expect questions on when async is actually helpful, what its limits are, and even troubleshooting common gotchas (like using the wrong library type). Practice building both sync and async endpoints, and you’ll be ready for anything.

7. Middleware Implementation

Middleware kind of sits in the middle of everything in a FastAPI app—every request and response passes through it before anything else happens. Basically, middleware lets you modify, inspect, or log what’s going on as requests come in and go out. If you want to add things like authentication checks, custom headers, logging, or timing, middleware is where that happens.

Here’s how you’d typically go about using middleware in FastAPI:

1. Write a function (or class) that takes a request, does something (like log it, check headers, catch errors, etc.), and then either passes the request along or handles it directly.
2. Decorate the function—or register the class—with FastAPI’s built-in add_middleware() function or the @app.middleware("http") decorator if you’re writing a function-based one.
3. Return the modified or original response.

Some real-world uses you’ll run into:

• Logging request and response details for debugging or audits.
• Catching and formatting errors before they hit the user.
• Adding or checking for authentication tokens on every endpoint.
• Timing how long requests and responses take (which is helpful for performance stuff).

Here’s a table comparing two common approaches for implementing middleware in FastAPI:

Approach	How It Works	Use Case Examples
Function-based	Uses @app.middleware("http") to run a sync or async function before/after requests	Quick logging, timing, simple auth
Class-based	Add with app.add_middleware(); can handle init params and more complex logic	CORS, sessions, advanced error handling

Honestly, once you get the hang of middleware, it’s one of those features you end up using all the time. It’s super flexible but don’t overdo it—keep things clean, and you’ll be fine.

8. Error Handling Strategies

There’s always that moment in building APIs when something goes wrong—maybe a request comes in weird, or you’re pulling data and hit a wall. Handling errors cleanly in FastAPI isn’t just about fixing problems—it’s about giving developers and users useful messages and keeping things running smoothly. Missing this step can make life miserable for everyone who works with your API, and that’s something you want to avoid.

Common ways to manage errors in FastAPI include:

• Using custom exception handlers to produce neat, clear responses for specific issues (think 404s, validation mess-ups, or authentication flubs)
• Taking advantage of FastAPI’s built-in HTTPException for quick, custom error responses with status codes and details
• Logging errors with enough details so you can find and fix issues without leaking sensitive data

Here’s a small table showing typical HTTP errors and how you might catch or handle them:

Error Type	Status Code	Where You Handle It
Not Found	404	route-level or global handler
Validation Error	422	automatic (Pydantic)
Unauthorized	401/403	dependencies or middleware
Internal Server Error	500	global exception handler

It helps to have strategies for the unexpected, too. For example, using a global handler to catch anything not covered by specific cases. This stopgap means that unhandled mistakes won’t break your service—think of it like that backup wrench in the toolbox you always reach for at the worst possible time.

A good approach to error handling also means making it consistent. If your error messages jump all over the place in their format, developers won’t know what to expect. Standardizing error shapes (say, always having a ‘message’, ‘code’, and maybe a ‘details’ field) makes debugging so much easier for the team. Keeping these routines tight helps in other areas, too, like avoiding common mistakes around process slack and missing documentation.

Make sure to review your error handling setup every couple months—it’s easy for new bugs to sneak in as your API grows or changes. Having a plan and actually sticking to it will save a lot of headaches.

9. Authentication and Authorization

If you want to build any real web app or API, you have to think about how to keep it secure—meaning, figuring out who should have access to what, and blocking everyone else. FastAPI doesn’t come with authentication systems out of the box, but it makes plugging them in pretty straightforward. Picking the right authentication method depends a lot on your use case.

Here are some standard approaches you’ll run into:

• OAuth2 with JWT Tokens: You’ll see this approach everywhere, especially for APIs that need to be stateless and secure. Clients get a token and include it in their requests so the server knows who’s calling.
• HTTP Basic Auth: Simple, but not very secure for production. Still, it’s okay for internal tools or quick prototypes.
• API Keys: Generating and checking API keys can work for service-to-service communication or simple use cases.

Let’s break down the steps to add authentication and authorization to a FastAPI app:

1. Decide on your authentication strategy (like OAuth2, API keys, or session-based).
2. Add dependencies that check user credentials—FastAPI’s dependency system makes this easy.
3. Set up permission checks for each route or set of routes. You can restrict sensitive endpoints to certain users or roles only.

FastAPI’s docs and community have a lot of code samples, but if you want some solid real-world code and practical examples for FastAPI API security, this resource can speed things up.

A common mistake: people mix up authentication ("who are you?") and authorization ("what can you do?"). Keep these checks separate—first make sure you know the user, then check what they’re allowed to access. Most apps need both.

For reference, here’s a quick table showing common authentication methods and pros/cons:

Method	Good For	Not So Good For
OAuth2 + JWT	Distributed REST APIs	Super simple internal tools
HTTP Basic Auth	Prototyping, quick demos	Production (unsafe)
API Keys	Machine-to-machine, scripts	Detailed user permissions

Getting authentication and authorization right isn’t glamorous, but it’s one of the most important things you’ll deal with when using FastAPI in any serious setting.

10. Deployment Best Practices

If you’re building something with FastAPI, sooner or later you’ll need to get it running outside your laptop. How you deploy your app matters a lot—the details can make or break its reliability and speed. There are so many options, but here are some of the usual suspects and what you should probably consider:

• Containerization: Most folks use Docker. It keeps your environment consistent, so your app runs the same way on your computer and in the cloud. No need to debug those "but it worked on my machine" problems.
• ASGI Servers: FastAPI is built for ASGI. Uvicorn and Hypercorn are the most common choices to actually serve your app. Don’t use the built-in server from FastAPI for production—it’s fine for development, but that’s all.
• Reverse Proxies: In real-world deployments, your app will sit behind something like Nginx or Caddy. This lets you handle HTTPS, manage static files, and deal with load balancing.
• Environment Variables: Don’t hard-code settings like database passwords. Use environment variables or tools like Pydantic’s Settings to manage configuration safely.
• Monitoring & Logging: This is where most people cut corners. At minimum, you’ll want error logs, access logs, and something to track performance (response time, error rates). Look into Prometheus, Grafana, or even simpler logging setups if you want an easier start.
• Scaling: If you expect more users, plan for auto-scaling. Kubernetes is one way to do this, but cloud-specific tools like AWS ECS or GCP Cloud Run can be simpler.
• CI/CD: Set up an automated pipeline with GitHub Actions, GitLab CI, etc. That way, every push gets tested and deployed automatically. No excuses for manual deploys.

Here’s a table of a few common FastAPI deployment options and what they’re good at:

Deployment Option	Pros	Cons
Docker + Uvicorn/Nginx	Portable, reliable, highly reusable	Needs setup effort
Serverless (AWS Lambda)	Good for bursty workloads, cheap idle	Cold starts, limits
Kubernetes	Scalable, good for big teams/apps	Complex to maintain
Managed cloud	Easy, quick setup, support included	Can cost more over time

Getting all this right takes a bit of trial and error. Sometimes things break even when you think you’ve done everything by the book. That’s just how deployment goes. But if you cover the basics above, you’ll save yourself a lot of pain down the road.

Wrapping Up

Alright, that’s a wrap on our list of FastAPI interview questions for 2025. If you’ve made it this far, you’re probably feeling a bit more ready for your next interview—at least, I hope so. FastAPI is showing up everywhere these days, and companies really want folks who can build APIs that are quick, reliable, and easy to maintain. My advice? Don’t just memorize answers. Try building a few small projects, mess around with the docs, and see what breaks (trust me, something always does). Interviews can be stressful, but a little hands-on practice goes a long way. Good luck out there, and if you land that job, maybe treat yourself to something nice—like a pizza. You’ve earned it.

Frequently Asked Questions

What is FastAPI and why is it popular?

FastAPI is a modern web framework for building APIs with Python. It is popular because it is fast, easy to use, and helps you write clean code. FastAPI also checks your data and gives automatic docs, which saves time for developers.

How does FastAPI handle dependency injection?

FastAPI uses a simple way to add things your functions need, like database connections or security checks. You just add them as function arguments, and FastAPI will give them to you when needed. This keeps your code tidy and easy to test.

What are path and query parameters in FastAPI?

Path parameters are parts of the URL that change, like an ID in ‘/users/5’. Query parameters are added after a question mark in the URL, like ‘/search?name=John’. FastAPI makes it easy to get both types in your Python functions.

How does FastAPI use Pydantic for data validation?

FastAPI works with Pydantic to check that the data sent to your API is correct. You can make models with Pydantic, and FastAPI will make sure the data fits your rules before using it. This helps catch mistakes early.

Can FastAPI handle asynchronous code?

Yes, FastAPI works great with async code. You can write your functions using ‘async def’, which helps your app handle many users at once without getting slow. This is useful for things like talking to databases or other APIs.

What is the best way to deploy a FastAPI app?

The best way is to use a server like Uvicorn or Gunicorn. You should also use Docker to package your app and make sure it runs the same everywhere. For bigger projects, it’s good to use cloud services like AWS or Azure to make your app easy to scale and manage.