FastAPI Interview Questions and Answers: Part 5 — Advanced FastAPI, Performance, Microservices & Deployment

We have covered FastAPI fundamentals, validation, asynchronous programming, Dependency Injection, WebSockets, background tasks, middleware, CORS, testing, authentication, JWT, OAuth2, and databases in the previous parts.

Now we move to the advanced and production side of FastAPI.

At this level, interviewers usually stop asking only about individual FastAPI features. They start asking questions like:

• How would you make an API faster?
• How would you handle thousands of requests?
• How would you design microservices?
• How would you cache frequently requested data?
• How would you deploy FastAPI?

1. Explain how to implement custom request validation and serialization beyond Pydantic defaults.

Pydantic already provides a lot of validation.

For example:

This tells FastAPI:

But sometimes our application has special rules.

For example:

A password must contain at least 8 characters.

That is not just a basic data-type check. It is a business rule.

We can create custom validation for such cases.

Now:

will fail validation.

But:

can pass.

Why do we need custom validation?

Because real applications have rules such as:

So there are two levels:

The source material describes custom validators and serialization as useful when applications have complex business rules, need data transformation, or require additional security checks.

2. How do you design FastAPI microservices and handle inter-service communication?

Imagine you build a large shopping application.

It may contain:

One option is to put everything into one large application.

Another option is to divide it into smaller services.

For example:

Each service handles a specific business responsibility.

This is called microservices architecture.

How do services communicate?

There are several ways.

REST APIs

One service can call another using HTTP.

Message queues

Services can communicate through a message broker.

Examples include:

For example:

The services don't have to directly wait for each other in every situation.

Service discovery

In a larger environment, services may run on different machines or containers.

Service discovery helps one service find another service.

Important design considerations

A good microservice design should consider:

• Independent deployment
• Clear API contracts
• Service ownership
• Authentication
• Logging
• Failure handling
• Database boundaries

The source specifically highlights independent deployment, separate databases, clear API contracts, centralized authentication/logging, REST APIs, message queues, and service discovery.

Important interview point

Microservices are not automatically better than a monolith.

If the application is small, creating ten different services can make the system unnecessarily complicated.

So the real question is:

Does the application actually benefit from separating its responsibilities into independent services?

3. What strategies do you use for caching in FastAPI applications?

Imagine your API receives this request:

The API queries the database.

Then another user requests the same product.

The API queries the database again.

And again.

And again.

If the data doesn't change frequently, repeatedly asking the database is unnecessary.

This is where caching helps.

Caching means storing frequently used data somewhere faster so that we don't have to calculate or fetch it again every time.

Common caching approaches

1. In-memory caching

Data is stored inside application memory.

This can be simple and fast, but it has limitations.

If you have multiple FastAPI instances:

each server may have a different cache.

2. Redis

Redis is a separate in-memory data store that is commonly used for caching.

Multiple application instances can share the same cache.

3. Response caching

Instead of caching only database data, we can cache an entire API response for suitable endpoints.

Simple example

Suppose:

takes 2 seconds because it performs a complicated database query.

If the result is cached for a short period:

the API can respond much faster.

But caching has a problem

What happens if the database changes?

The cache might still contain old information.

This is called stale data.

So caching always requires thinking about:

The source describes in-memory caching, Redis caching, and response caching as common strategies, with caching reducing repeated database or external API calls.

4. How do you deploy a FastAPI application with Docker and Uvicorn/Gunicorn?

Writing an API is only one part of development.

Eventually, we need to run it on a server.

A common deployment setup is:

What does Docker do?

Docker packages the application and its environment into a container.

Instead of saying:

"It works on my laptop."

we can create a consistent environment containing:

A simple Dockerfile might look like:

Then the application can run inside a container.

What is Uvicorn?

Uvicorn is an ASGI server commonly used to run FastAPI applications.

For example:

What about Gunicorn?

Gunicorn is a process manager/server commonly used with worker processes. A common traditional setup uses Uvicorn workers.

Conceptually:

The exact deployment architecture depends on the environment and current server tooling.

Why use containers?

Docker can help with:

• Consistent environments
• Easier deployment
• Scaling
• CI/CD
• Dependency isolation

The source material specifically describes Docker containerization and Uvicorn/Gunicorn-based deployment, including Uvicorn workers. 

5. Explain lifespan events (startup/shutdown) in FastAPI and their use cases.

Some resources need to be prepared when the application starts.

For example:

And when the application stops:

FastAPI provides lifespan handling for this application lifecycle.

A simple modern example is:

The important part is:

Everything before yield is startup work.

Everything after yield is shutdown/cleanup work.

Conceptually:

Common use cases

Lifespan handling can be useful for:

• Loading machine learning models
• Initializing caches
• Preparing connections
• Starting resources
• Closing resources
• Cleaning up application state

The source identifies database connections, machine-learning models, caches, and message queues as examples of resources that can be initialized or cleaned up during the application lifecycle.

6. How do you implement rate limiting and request throttling in FastAPI?

Imagine someone sends:

to your API within a minute.

That can overload the server.

It can also be a sign of abuse or an automated attack.

Rate limiting controls how many requests a client is allowed to make during a particular period.

For example:

means that a client can make five requests within the configured time window.

A common approach is to use a library such as slowapi.

Conceptually:

Now the endpoint has a request limit.

What can we limit by?

We can consider:

Where is rate-limit information stored?

For a simple application, it might be stored locally.

For a distributed application:

A shared store such as Redis can help multiple servers keep track of request counts consistently.

Why is rate limiting useful?

It helps:

• Protect server resources
• Reduce abuse
• Prevent excessive requests
• Improve API stability

The source specifically describes rate limiting as a way to prevent server overload and abuse and mentions middleware, slowapi, IP-based limits, user-based limits, and Redis-backed tracking.

7. How do you handle exceptions globally in FastAPI?

Suppose ten different endpoints can produce the same type of application error.

We could handle every error separately:

This can create repetitive code.

Instead, FastAPI allows us to create global exception handlers.

For example:

Now whenever this exception is raised:

FastAPI can use the registered handler.

Why is this useful?

Suppose your API has a standard error format:

A global exception handler can help keep errors consistent across the application.

Simple flow

This makes large applications easier to maintain.

The source specifically describes custom exception classes together with @app.exception_handler() for global exception handling.

8. How can you optimize the performance of FastAPI?

This is one of the questions where a good interview answer should not immediately say:

"Use async."

Performance problems can come from many different places.

Imagine:

If the database query takes 5 seconds, making the FastAPI route asynchronous doesn't magically make the database query itself faster.

So the first step is:

Find the actual bottleneck.

Common areas to investigate

Possible improvements

1. Use async for suitable I/O

For I/O-bound operations, asynchronous programming can improve concurrency.

2. Optimize database queries

Instead of:

use appropriate filtering and pagination.

3. Add caching

Frequently requested data can sometimes be cached.

4. Use efficient middleware

Middleware runs around requests, so unnecessarily expensive middleware can affect performance.

5. Use load balancing

If one server cannot handle the traffic:

Traffic can be distributed across multiple application instances.

6. Measure before optimizing

Use logs, metrics, tracing, and profiling to identify the real problem.

The source specifically lists asynchronous endpoints for I/O-bound operations, caching, efficient middleware, and load balancers such as Nginx or Traefik as performance strategies.

9. How do you handle large file uploads in FastAPI?

Imagine a user uploads:

That's usually manageable.

Now imagine:

We should not treat a huge file exactly like a tiny JSON request.

FastAPI provides UploadFile for file uploads.

Example:

UploadFile provides a file-like interface and is suitable for efficient file handling.

Why is this important?

Large files require careful memory and resource management.

A common approach is to process the file in chunks rather than trying to load the entire file into memory at once.

Conceptually:

For very large uploads, you may also need to consider:

• Maximum upload size
• Timeouts
• Storage location
• Streaming
• Temporary files
• Cloud/object storage
• Network reliability

The source specifically mentions UploadFile, ASGI configuration, StreamingResponse, and processing files in chunks for large uploads.

10. What is the difference between a monolithic FastAPI application and a microservices-based FastAPI system?

This is a good architecture-level interview question.

Monolithic application

Everything exists inside one application.

You deploy the application as one unit.

This can be a good choice for smaller applications because it is easier to develop and deploy.

Microservices

The application is divided into separate services.

Each service can potentially be:

• Developed independently
• Deployed independently
• Scaled independently

Services communicate using APIs or messaging systems.

Example

Suppose the payment system receives huge traffic but the user-profile system receives very little.

With microservices:

You don't necessarily need to scale the entire application.

But microservices have a cost

They introduce additional complexity:

So:

Monolith → simpler system with everything together.

Microservices → independently deployable services with more operational complexity.

The source describes microservices as independent services with separate deployment and communication through REST APIs or message queues.

The Complete FastAPI Journey

We started with basic questions and gradually moved toward production-level concepts.

Part 1 — Fundamentals

Part 2 — Core Development

Part 3 — Application Features

Part 4 — Security & Database

Part 5 — Production & Architecture

At this point, the important thing is not to memorize every FastAPI function.

You should be able to look at a real application and reason about it:

And when something becomes slow or fails, you should be able to ask:

Where exactly is the problem?

Is it authentication?

Is it validation?

Is the database slow?

Is an external API taking too long?

Do we need caching?

Are we receiving too many requests?

Do we need more application instances?

Should this functionality become a separate service?

That way of thinking is what takes FastAPI knowledge from “I know the framework” to “I can build and reason about a backend system.” The advanced source set similarly emphasizes performance, caching, deployment, microservices, rate limiting, and production concerns.

Final Interview Tip

For beginner questions, a short definition may be enough.

For advanced questions, interviewers usually want to know why you would choose something, what problem it solves, and what trade-offs it introduces.

For example:

“Why use Redis?”

Don't stop at:

“Redis is a cache.”

A stronger answer is:

“If the same data is requested repeatedly, Redis can store the result so the application doesn't have to query the database every time. This can reduce database load and improve response time. However, we also need to think about cache expiration and stale data.”

That difference — definition → reason → example → trade-off — is what makes an advanced interview answer stronger.