Top 33 FastAPI Interview Questions and Answers 2025

FastAPI has quickly become a go-to framework for building high-performance, scalable web applications with Python. Its emphasis on speed, ease of use, and automatic validation has made it a popular choice among developers looking to efficiently deploy robust APIs. As the demand for FastAPI expertise grows in the technology sector, understanding its core principles and functionalities can significantly enhance your career prospects.

Preparing for an interview that covers FastAPI requires a solid grasp of its features, best practices, and common use cases. This collection of the top 33 FastAPI interview questions and answers is designed to help you articulate your knowledge and experience with the framework. Whether you’re a seasoned developer or new to the world of FastAPI, these insights will help you approach your next interview with confidence.

FastAPI Interview Preparation Tips

Focus Area	Details	Tips
Understanding FastAPI	FastAPI is a modern, fast (high-performance) web framework for building APIs with Python 3.7+ based on standard Python type hints.	Make sure you are comfortable with Python, especially with async features and type hints. Study the official FastAPI documentation.
ASGI	FastAPI is built on top of Starlette for the web parts and uses Pydantic for the data parts. It’s an ASGI framework.	Understand ASGI (Asynchronous Server Gateway Interface) and its differences from WSGI.
API Design	Designing APIs with FastAPI, including path operations, query parameters, body requests, and response models.	Practice designing simple APIs. Learn how to use Pydantic models for request and response data.
Authentication & Security	FastAPI provides several tools and mechanisms to secure your application, such as OAuth2 with Password (and hashing), JWT tokens, and others.	Understand OAuth2 flow, how JWT tokens are used in FastAPI, and how to implement security schemes.
Database Integration	Integrating databases with FastAPI applications, using ORMs like SQLAlchemy or data access libraries like databases for async support.	Get familiar with async database libraries and how to use them in FastAPI for CRUD operations.
Dependency Injection	FastAPI supports dependency injection as a way to provide shared logic (like database session management) and data (like current user) across path operations.	Learn how to use dependencies for various scopes and how to override them for testing.
Testing FastAPI Applications	Testing is crucial for any application. FastAPI provides tools for testing with Pytest.	Learn how to write tests for API endpoints, including how to use TestClient for test requests.
Performance & Scaling	Understanding how to monitor, analyze, and improve the performance of FastAPI applications. Knowledge about deploying and scaling FastAPI apps.	Explore profiling tools, async features, and deployment options like Docker that help in scaling.

Each of these focus areas is critical for preparing for a FastAPI interview. Dive deep into the official FastAPI documentation, build sample projects, and practice writing tests to strengthen your understanding and skills.

1. What Is FastAPI, and What Are Its Key Features?

Tips to Answer:

• Focus on explaining FastAPI as a modern, fast (high-performance) web framework for building APIs with Python 3.7+ based on standard Python type hints.
• Highlight a few key features like automatic API documentation, data validation, and asynchronous support, which demonstrate its efficiency and ease of use.

Sample Answer: FastAPI is a contemporary web framework designed to create APIs with Python 3.7 or newer, emphasizing speed and rapid development. One of its standout features is the automatic generation of interactive API documentation using Swagger UI and ReDoc, making it incredibly user-friendly for both developers and end-users. Additionally, it leverages Python type hints for data validation, ensuring that the data exchanged through your API meets your specifications. This not only helps in catching errors early but also significantly simplifies the codebase by reducing boilerplate for input serialization and validation. FastAPI supports asynchronous request handling out of the box, allowing for scalable and high-performing applications by efficiently managing long-running operations and high-load situations. This blend of performance, ease of use, and robust functionality makes FastAPI a highly appealing choice for modern web API development.

2. How Does FastAPI Handle Request Validation?

Tips to Answer:

• Highlight FastAPI’s built-in request validation capabilities, emphasizing its use of Pydantic models for schema definition and automatic validation.
• Mention the benefits of having automatic documentation that reflects these validation schemas, streamlining the development and integration process.

Sample Answer: In FastAPI, request validation is seamlessly managed through the use of Pydantic models. When defining an endpoint, I specify the expected request body by referencing a Pydantic model. This model outlines the structure, types, and even validation constraints for the data. FastAPI then automatically validates incoming requests against this model before the request even reaches my endpoint logic. If the request data does not conform to the defined schema, FastAPI automatically returns a detailed error response, indicating exactly what was wrong. This not only ensures that my application receives data in the expected format but also significantly reduces the amount of manual validation code I need to write. The integration of these models with FastAPI’s automatic documentation means that the expected request format is clearly communicated to anyone using the API, further smoothing the development process.

3. What Is Dependency Injection In FastAPI?

Tips to Answer:

• Discuss how FastAPI’s dependency injection system simplifies the development of complex web applications by promoting reusable and maintainable code.
• Highlight the benefits of using dependency injection, such as easier testing and the ability to share common logic across different parts of the application.

Sample Answer: In FastAPI, dependency injection is a powerful feature that allows me to declare and manage dependencies independently from my path operation functions. This means I can use functions, database sessions, or other objects across my application without directly coupling them to my endpoint logic. By specifying dependencies in my path operation decorators, FastAPI takes care of providing the required elements when a request is processed. This approach not only makes my code cleaner and more modular but also significantly eases the testing process. I can easily swap out real dependencies with mocks or fakes during testing, ensuring that my tests are fast and reliable. Dependency injection in FastAPI has been instrumental in developing scalable and efficient web services, as it allows me to reuse common functionality and ensure my application remains easy to maintain as it grows.

4. How Does FastAPI Handle Asynchronous Programming?

Tips to Answer:

• Emphasize FastAPI’s built-in support for asynchronous request handling, highlighting how this can improve the efficiency of web applications by allowing them to process multiple requests simultaneously.
• Mention specific examples or features, such as the use of async and await keywords, to illustrate how FastAPI facilitates asynchronous programming.

Sample Answer: In FastAPI, asynchronous programming is a core feature that significantly enhances the performance of web applications. By integrating seamlessly with Python’s asyncio library, FastAPI allows me to write asynchronous code using the async and await syntax. This approach enables the server to handle multiple requests at the same time without waiting for one to complete before starting another. Consequently, my web applications become more scalable and responsive. Implementing asynchronous database calls, background tasks, or even interacting with external APIs becomes straightforward with FastAPI, ensuring that I can optimize I/O-bound operations efficiently.

5. How Does FastAPI Generate Interactive API Documentation?

Tips to Answer:

• Highlight the automatic generation of documentation and its benefits for both developers and users.
• Explain the role of OpenAPI and how FastAPI leverages it to create and customize the interactive API docs.

Sample Answer: FastAPI automatically generates interactive API documentation using OpenAPI specifications. This feature is incredibly beneficial as it offers a web interface where developers and users can see all the available endpoints, their expected request formats, and even try them out in real-time. The documentation is updated dynamically with every change in the code, ensuring it always reflects the most current state of the API. By leveraging OpenAPI, FastAPI allows for easy customization of the documentation, enabling me to tailor it to meet the specific needs of my projects and make it more user-friendly. This auto-generated documentation significantly streamlines the development process, fosters collaboration, and enhances the overall usability of the API.

6. How Can You Handle Cross-Origin Resource Sharing (CORS) in FastAPI?

Tips to Answer:

• Highlight the importance of CORS for web application security and how FastAPI simplifies its management.
• Discuss the use of FastAPI’s CORSMiddleware and how to configure it effectively for different scenarios.

Sample Answer: In my projects, I’ve found handling Cross-Origin Resource Sharing (CORS) pivotal for allowing my web applications to securely request resources from different origins. FastAPI makes this process straightforward with the CORSMiddleware from the fastapi.middleware.cors module. I usually start by importing CORSMiddleware and then add it to my application, specifying the origins allowed to communicate with the API. This setup involves defining a list of origins, or using ["*"] to allow all origins, which I only recommend for development. I also configure which methods and headers are allowed, and whether credentials are supported. This flexibility lets me tailor CORS settings to meet my application’s security and functionality requirements.

7. Does FastAPI Support Database Integration?

Tips to Answer:

• Highlight specific libraries or ORM tools that FastAPI can integrate with for database operations.
• Mention personal experience or examples demonstrating how database integration can be achieved in FastAPI projects.

Sample Answer: Yes, FastAPI supports database integration seamlessly. I’ve integrated databases into my FastAPI projects using SQL databases through SQLAlchemy and async ORM tools like Tortoise ORM for asynchronous support. For instance, in a project requiring high-performance data transactions, I utilized SQLAlchemy with Alembic for migrations to manage a PostgreSQL database efficiently. This setup allowed me to leverage FastAPI’s asynchronous capabilities, ensuring non-blocking database operations, which significantly improved the API’s response times. Connecting FastAPI with a database involves defining models, schemas, and database sessions, which are well-documented in FastAPI’s extensive documentation. My experience has shown that this integration not only enhances performance but also simplifies the development process.

8. How to Handle Cross-Origin Resource Sharing (CORS) in FastAPI?

Tips to Answer:

• Understand the concept of CORS and why it’s critical for web security. Be ready to explain how CORS works and what problems it solves, especially in a FastAPI context.
• Be specific about the steps or code snippets to enable CORS in FastAPI. Knowing the fastapi.middleware.cors module and how to use it effectively can set you apart.

Sample Answer: In FastAPI, handling Cross-Origin Resource Sharing (CORS) is straightforward thanks to built-in middleware. To set it up, I import CORSMiddleware from fastapi.middleware.cors and add it to my FastAPI application. I specify the origins allowed to communicate with my API by setting the allow_origins parameter. This can be a list of specific domains or ["*"] for allowing all. I also configure what resource sharing policies my API should adhere to, including allowed methods and headers. This setup ensures my API can be safely accessed from web applications hosted on different domains, maintaining security while allowing necessary interactions.

9. How to Upload A Large File (≥3GB) to FastAPI Backend?

Tips to Answer:

• Mention the use of asynchronous file handling to ensure non-blocking operations.
• Highlight the importance of configuring the server for large file sizes and possibly using a streaming approach.

Sample Answer: In handling the upload of large files, such as those over 3GB, to a FastAPI backend, I start by ensuring the server is configured to accept large payloads. This involves adjusting parameters such as request size limits in the FastAPI application and the web server (e.g., Uvicorn or Gunicorn) settings. I use asynchronous file handling within my endpoint to read the incoming file in chunks. This method allows the server to remain responsive, as it doesn’t block other operations while handling the large file. I might also consider a streaming approach, where the file is processed and stored in parts, reducing memory usage and improving the efficiency of the upload process. This strategy is crucial for maintaining the performance and reliability of the application when dealing with large file uploads.

10. How to Post JSON Data From JavaScript Frontend to FastAPI Backend?

Tips to Answer:

• Focus on explaining the step-by-step process, starting from the JavaScript code to send the request, and then how to handle it in FastAPI.
• Highlight the importance of setting the correct content type in the request headers and parsing JSON data appropriately in FastAPI.

Sample Answer: In my experience, posting JSON data from a JavaScript frontend to a FastAPI backend involves a few crucial steps. On the frontend side, I use the Fetch API or XMLHttpRequest to send data. I ensure to set the Content-Type header to application/json. Here’s a snippet:

fetch('http://example.com/api/data', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({key: 'value'}),
})

On the FastAPI side, I define an endpoint that expects a JSON body. I use Pydantic models to parse and validate the incoming data effortlessly. This approach provides automatic documentation and validation, which significantly simplifies the data handling process:

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Item(BaseModel):
    key: str

@app.post("/api/data")
async def create_item(item: Item):
    return {"received": item.key}

This method ensures that the data flow between frontend and backend is smooth and secure.

11. How to Return Data in JSON Format Using FastAPI?

Tips to Answer:

• Highlight FastAPI’s built-in support for data serialization and the ease of converting Python objects to JSON.
• Mention the use of Pydantic models for data validation and serialization, emphasizing how it automatically converts the output to JSON without extra work from the developer.

Sample Answer: In FastAPI, returning data in JSON format is straightforward thanks to the framework’s automatic serialization. When I define my endpoint, I often use Pydantic models to represent the data structure I want to return. This approach not only helps in validating the incoming request data but also in serializing the response data to JSON effortlessly. For example, when a client hits my endpoint, I process their request, and my function returns a Pydantic model or a list of models. FastAPI takes care of converting these models to JSON. This eliminates the need for manual serialization, making the development process smoother and more efficient.

12. How to Log Raw HTTP Request/Response in Python FastAPI?

Tips to Answer:

• Highlight the importance of logging for debugging and monitoring purposes in a FastAPI application.
• Mention specific middlewares or libraries that can be utilized for logging HTTP requests and responses.

Sample Answer: In FastAPI, logging raw HTTP requests and responses is crucial for debugging and gaining insights into the application’s performance. I usually implement custom middleware or utilize libraries such as starlette-context which allows logging important details without much hassle.

For instance, I create a middleware that captures incoming requests and outgoing responses, then logs the necessary information like the request path, method, and status code of the response. This approach not only helps in monitoring the application but also aids in quickly pinpointing issues. I ensure that sensitive information is either not logged or properly anonymized to adhere to privacy standards.

13. How to Add Both File and JSON Body in a FastAPI POST Request?

Tips to Answer:

• Highlight the importance of using Form and File parameters from FastAPI to handle multipart requests.
• Mention the use of Pydantic models for JSON body validation to ensure data integrity and simplify the request handling process.

Sample Answer: In handling a POST request in FastAPI that requires both a file and a JSON body, I start by defining a Pydantic model for the JSON data to leverage FastAPI’s automatic request validation. For the route, I specify parameters for both the file, using File(...), and the JSON body, using Form(...). This approach ensures that the endpoint can receive multipart/form-data requests properly. Here’s a quick example:

from fastapi import FastAPI, File, Form, UploadFile
from pydantic import BaseModel

app = FastAPI()

class Item(BaseModel):
    name: str
    description: str

@app.post("/upload/")
async def upload_file(item: Item = Form(...), file: UploadFile = File(...)):
    return {"filename": file.filename, "item_name": item.name, "description": item.description}

This method enables my API to accurately process incoming requests with both a file and structured JSON data, ensuring efficient and error-free data handling.

14. How to Initialize a Global Object or Variable and Reuse It in Every FastAPI Endpoint?

Tips to Answer:

• Illustrate your understanding of Python’s global variables or singleton pattern to maintain state across FastAPI endpoints.
• Emphasize the importance of thread safety and how you ensure that the global object’s state is consistent across requests.

Sample Answer: In my projects, I often use a singleton pattern to initialize a global object that I can reuse across all FastAPI endpoints. For instance, if I’m working with a database connection pool, I create a global variable in a separate module. This module is then imported wherever the connection pool is needed. Here’s a basic example:

# db_singleton.py
from some_database_library import ConnectionPool

pool = ConnectionPool()

# main.py
from fastapi import FastAPI
from db_singleton import pool

app = FastAPI()

@app.get("/")
def read_root():
    with pool.get_connection() as conn:
        # Use the connection
        pass

This approach ensures that the object is initialized once and reused, saving resources. I’m always careful to ensure that the global object is thread-safe, using locks if necessary, to maintain consistency across asynchronous requests.

15. Is Having a concurrent.futures.ThreadPoolExecutor Call Dangerous in a FastAPI Endpoint?

Tips to Answer:

• Mention the importance of understanding the FastAPI’s asynchronous nature and how ThreadPoolExecutor can affect performance and scalability.
• Highlight the need for careful consideration of thread management and potential blocking operations when integrating ThreadPoolExecutor in FastAPI.

Sample Answer: In my experience, using concurrent.futures.ThreadPoolExecutor in a FastAPI endpoint requires caution. FastAPI is designed to work well with asynchronous operations, making it highly efficient and scalable for handling web requests. Introducing ThreadPoolExecutor can be beneficial for offloading blocking IO operations to maintain responsiveness. However, it’s crucial to manage the threads wisely. Excessive use or mismanagement can lead to degraded performance due to context switching and can exhaust server resources, leading to scalability issues. I always ensure to limit the thread pool size according to the server’s capacity and specifically use it for operations that are truly blocking. This approach helps in leveraging the benefits of ThreadPoolExecutor without compromising the efficiency of FastAPI applications.

16. What Is the Proper Way To Make Downstream HTTPS Requests Inside Of Uvicorn/FastAPI?

Tips to Answer:

• Ensure to focus on the importance of asynchronous requests when discussing downstream HTTPS requests in a FastAPI application hosted by Uvicorn.
• Highlight the use of HTTP client libraries that support asynchronous operations, such as httpx, to maintain the efficiency and speed of your FastAPI application.

Sample Answer: In my experience, making downstream HTTPS requests within a FastAPI app running on Uvicorn requires attention to asynchronous programming. I use httpx, an async HTTP client library, which aligns with FastAPI’s async capabilities. This approach ensures that my application remains non-blocking and responsive, even when handling multiple downstream requests. By leveraging httpx, I can efficiently make HTTPS calls without disrupting the performance of my FastAPI application. It’s crucial to structure these requests correctly to maintain the app’s scalability and speed.

17. How to Upload a File Using FastAPI?

Tips to Answer:

• Emphasize your understanding of FastAPI’s built-in support for file uploads, including the use of File and UploadFile types from FastAPI’s fastapi module.
• Mention any specific experiences you have with handling file uploads in a FastAPI application, such as validating file types or sizes, or processing uploaded files asynchronously.

Sample Answer: In FastAPI, uploading a file is straightforward thanks to its comprehensive request handling system. When I need to implement a file upload feature, I start by importing File and UploadFile from the fastapi module. I prefer UploadFile because it’s async and saves memory for large files. Here’s a basic example:

from fastapi import FastAPI, File, UploadFile

app = FastAPI()

@app.post("/uploadfile/")
async def create_upload_file(file: UploadFile = File(...)):
    return {"filename": file.filename}

In this setup, the endpoint /uploadfile/ accepts a file upload. UploadFile has several useful attributes and methods, such as filename for the original file name, and it supports asynchronous operations. I ensure to validate the file size or type if needed, usually by checking the file’s content type or using custom validation logic before processing the file further. This method efficiently handles uploads, making them accessible and straightforward to manage in a FastAPI project.

18. How to Sort a Numerical List in Python Using FastAPI?

Tips to Answer:

• Focus on demonstrating your understanding of Python’s sort methods and how they can be integrated within a FastAPI endpoint.
• Mention the importance of efficient data handling and manipulation in building responsive APIs.

Sample Answer: In FastAPI, sorting a numerical list can be seamlessly integrated within an endpoint. Here’s how I typically handle it. First, I ensure the endpoint receives the list I need to sort. Python’s built-in sorted() function or the .sort() method on lists is my go-to for sorting. For instance, if I receive a list through a POST request, I’d parse the request body, extract the list, and apply sorted(list) to it. This sorted list can then be returned in the response. This approach ensures that my FastAPI endpoints can efficiently handle and manipulate data, providing sorted results wherever necessary.

19. How to Convert a List to a Tuple in FastAPI?

Tips to Answer:

• Discuss the simplicity and efficiency of converting lists to tuples in Python, emphasizing its relevance in FastAPI for scenarios where an immutable sequence of values is preferable.
• Highlight the significance of choosing the right data structure in FastAPI for enhancing the performance and security of your application, especially when dealing with data that should not be modified.

Sample Answer: In FastAPI, converting a list to a tuple can be a common requirement, especially when I need to ensure the immutability of data being processed. This might be crucial in scenarios where the data is being shared across different parts of the application and should remain unchanged. The process is straightforward in Python; I simply use the tuple() function and pass the list I want to convert as an argument. For example, if I have a list my_list = [1, 2, 3], converting it to a tuple would be as simple as doing my_tuple = tuple(my_list). This method is not only simple but also efficient, making it an excellent practice in FastAPI applications where data integrity and performance are key.

20. How to Achieve Multithreading in Python Using FastAPI?

Tips to Answer:

• Highlight your understanding of Python’s threading module and how it can be integrated into FastAPI for asynchronous tasks.
• Discuss the importance of thread safety and how to manage it when implementing multithreading in a FastAPI application.

Sample Answer: In FastAPI, I leverage the Python standard library’s threading module to implement multithreading. This allows me to perform multiple operations concurrently, improving the efficiency of my application. To ensure that my FastAPI application can handle multithreading, I pay careful attention to thread safety. This involves using thread-safe data structures and synchronizing access to shared resources to prevent race conditions. By effectively managing threads, I can enhance the responsiveness and throughput of my FastAPI applications, providing a better experience for end-users.

21. How to Remove the Last Object From a List in FastAPI?

Tips to Answer:

• Emphasize understanding of Python list operations as FastAPI relies heavily on standard Python features.
• Highlight practical experience with manipulating data structures within FastAPI endpoints, demonstrating problem-solving skills in real-world API development.

Sample Answer: In my projects using FastAPI, when I need to remove the last object from a list, I typically use Python’s built-in list methods. For instance, I would use the pop() method without an index, as it defaults to removing the last item of the list. This approach is both efficient and straightforward. Here’s a quick example:

my_list = [1, 2, 3, 4]
my_list.pop()

This snippet effectively removes the last element from my_list, adjusting the list to [1, 2, 3]. This method is particularly useful in FastAPI when processing or manipulating data received from client requests or before sending responses.

22. How to Reverse a List in FastAPI?

Tips to Answer:

• Highlight the simplicity and effectiveness of Python’s built-in methods or slices that can be used in FastAPI to manipulate data structures, such as lists.
• Emphasize the importance of understanding Python’s core functionalities to efficiently solve common programming tasks within FastAPI applications.

Sample Answer: In FastAPI, when I need to reverse a list, I typically rely on Python’s powerful and concise capabilities. For instance, if I have a list of items that I want to display in the reverse order from how they were inputted or stored, I would use the slice method. Here’s how I do it: reversed_list = my_list[::-1]. This method is not only straightforward but also efficient, as it doesn’t require me to import additional modules or write extensive code. Understanding and utilizing such Python features allow me to handle list manipulations effortlessly in any FastAPI project.

23. How to Generate Random Numbers in FastAPI?

Tips to Answer:

• Understand and be able to discuss the Python standard library’s random module and its integration within FastAPI endpoints.
• Explain how generating random numbers can be utilized in practical scenarios within FastAPI applications, for example, in token generation or in testing endpoints with random inputs.

Sample Answer: In FastAPI, generating random numbers is an operation that can be achieved by leveraging Python’s built-in random module. When I need to generate a random number within a FastAPI endpoint, I first import the random module. Then, depending on the requirement, I use random.randint() for integers or random.random() for floating-point numbers. For instance, to generate a random token, I might use random.randint(1000, 9999) to get a 4-digit code. This approach is especially useful in scenarios like OTP generation or simulating variable data for testing APIs. It’s a straightforward yet powerful method to introduce variability into our FastAPI applications.

24. What Advantage Do NumPy Arrays Have Over Nested Python Lists In FastAPI?

Tips to Answer:

• Highlight the performance and efficiency of NumPy arrays compared to traditional Python lists, especially in the context of FastAPI where processing speed can be critical.
• Discuss the importance of using NumPy for data-intensive operations within FastAPI endpoints, emphasizing on its ability to handle large datasets more effectively.

Sample Answer: In the realm of FastAPI, leveraging NumPy arrays instead of nested Python lists provides significant benefits, primarily due to their superior performance and efficiency. When developing FastAPI applications, processing speed is a vital factor, especially when dealing with data-intensive operations. NumPy arrays are specifically designed for numerical computation, offering optimized storage and faster computations compared to Python lists. This efficiency stems from the fact that NumPy arrays store data in a contiguous block of memory, allowing for vectorized operations and reducing the overhead of looping. Furthermore, NumPy arrays support broadcasting and advanced data manipulation techniques, which are indispensable for performing complex mathematical tasks and data analysis within FastAPI endpoints. Utilizing NumPy arrays ensures that my FastAPI applications are not only faster but also more scalable when handling large datasets, thereby enhancing the overall performance and user experience.

25. What Are Decorators in Python in the Context of FastAPI?

Tips to Answer:

• Understand the concept of decorators as functions that modify the behavior of other functions or methods without changing their code.
• Be able to explain how decorators can be used in FastAPI for tasks like authentication, routing, and response modification.

Sample Answer: In the context of FastAPI, decorators are a powerful feature of Python that allows me to modify or enhance the functionality of functions or methods without directly changing their code. For example, FastAPI uses decorators extensively to simplify API route creation. By using the @app.get() or @app.post() decorators, I can easily define endpoints. Additionally, I can use decorators to enforce authentication on specific endpoints, ensuring that only authenticated users can access certain functions. This makes my code cleaner, more modular, and easier to maintain, as I can reuse and apply common functionalities across different parts of my application.

26. What Are Python Modules And How Are They Used In FastAPI?

Tips to Answer:

• Understand and explain the concept of Python modules as reusable pieces of code that can be imported to add functionality to a FastAPI application.
• Highlight the importance of modular programming in building scalable and maintainable FastAPI applications.

Sample Answer: In my FastAPI projects, I leverage Python modules extensively to organize my code into manageable, reusable components. Modules in Python are essentially files containing Python definitions and statements. This structure allows me to compartmentalize different aspects of the application, like database models, schemas, utility functions, and service layers. By using modules, I ensure that my FastAPI application remains scalable and easy to maintain. For instance, I typically have a module for handling all database interactions and another module dedicated to business logic. This separation of concerns not only makes the codebase cleaner but also enhances collaboration among developers by allowing us to work on different modules without causing conflicts.

27. What Is Pickling And Unpickling In FastAPI?

Tips to Answer:

• Understand the concepts of serialization and deserialization, as they relate to pickling and unpickling.
• Provide examples of when you might use pickling and unpickling in a FastAPI project.

Sample Answer: In FastAPI, pickling is the process of converting a Python object into a byte stream, enabling it to be saved to a file or transmitted over a network. This is crucial for caching responses or storing session information in FastAPI applications. Unpickling is the reverse process, where the byte stream is converted back into a Python object. I’ve used pickling in FastAPI to efficiently cache data that was expensive to compute, such as results from database queries or external API calls. I ensure to use secure practices to avoid executing malicious code during the unpickling process, especially when the source of the pickle data is untrusted.

28. How Can You Share Global Variables Across Modules in FastAPI?

Tips to Answer:

• Highlight the importance of using global variables carefully to prevent unintended side-effects or hard-to-debug issues in a FastAPI application.
• Mention specific FastAPI or Python features, such as dependency injection or the use of a config module, which can be used to share global variables across modules effectively.

Sample Answer: In FastAPI, sharing global variables across modules requires careful consideration to ensure application stability. The key is to prevent direct modification of the global state from different parts of the application. I prefer to use a dedicated config module where I define all global variables. This module can then be imported wherever the variables are needed. Additionally, FastAPI’s dependency injection system offers a clean way to share and reuse global variables, such as database connections, by defining them as dependencies. This strategy keeps the global state accessible yet controlled, ensuring my Fast API applications remain maintainable and scalable.

29. How to Avoid a Newline When Using Multiple Print Statements in FastAPI?

Tips to Answer:

• Highlight the use of the end parameter in the print function to control the behavior of the newline character.
• Discuss the practical scenarios in FastAPI development where controlling newline characters in logging or console output could be beneficial.

Sample Answer: In FastAPI development, I often find myself needing to print multiple messages or data points on the same line, especially when logging request details or debugging. To achieve this, I use the print function’s end parameter. By default, print adds a newline character at the end, but setting end='' allows me to continue printing on the same line. For example, if I want to log the method and URL of a request without breaking into a new line, I’ll write print(request.method, end=' '); print(request.url, end=''). This technique is particularly useful in creating more readable and compact logs or output for monitoring FastAPI applications.

30. What Is The Default Data Type Of The Input() Method In FastAPI?

Tips to Answer:

• Highlight your understanding of Python’s input() function and its relevance in FastAPI.
• Discuss practical scenarios where converting the input data type is necessary for FastAPI applications.

Sample Answer: In FastAPI, when dealing with user inputs through the input() method, it’s essential to remember that this function, inherited from Python, always returns data as a string. This characteristic is crucial when processing and validating user input in FastAPI endpoints. For instance, if I’m expecting a numerical value from the user, I need to explicitly convert the input from a string to an integer or float. This step is vital for ensuring that the data types match my application’s requirements and for preventing type-related errors. In my experience, being mindful of the input’s data type has helped me design more robust and error-resistant FastAPI applications.

31. What Is Slicing in Python And How Is It Used in FastAPI?

Tips to Answer:

• Focus on explaining the concept of slicing in Python clearly and concisely, mentioning how it allows for selecting subsets of data from sequences (like lists, tuples, strings, etc.).
• Highlight the applicability and benefits of slicing for FastAPI development, such as manipulating request data or preparing responses.

Sample Answer: In Python, slicing is a technique that enables selecting parts of data from sequences, such as lists, strings, or tuples. It’s defined by the syntax [start:stop:step], where start is the index to begin slicing, stop is the index to end (not included), and step is the interval between elements. In FastAPI, slicing becomes particularly useful when dealing with request data or preparing data for responses. For instance, I might slice a list of query parameters to apply certain processing only to a subset. This feature of Python, when utilized in FastAPI, enhances data manipulation capabilities, making it easier to handle complex data structures efficiently.

32. What Does [::-1] Do in FastAPI?

Tips to Answer:

• Focus on explaining the general Python concept of slicing and its application in any FastAPI context.
• Highlight practical examples where such slicing could be useful in FastAPI projects, like reversing strings or lists.

Sample Answer: In Python, [::-1] is a slicing technique used to reverse the order of elements. In the context of FastAPI, this can be applied to strings, lists, or any sequence types handled within the endpoints or utility functions. For instance, if I need to reverse a URL slug or a list of items fetched from a database before sending it in the response, I’d use this slice operation. It’s concise and efficient, making the code cleaner and more readable. Whether I’m manipulating path parameters or processing JSON payloads, this technique proves invaluable for reversing sequences without needing additional functions or loops.

33. How Does Python Support Arguments Within A Function In FastAPI?

Tips to Answer:

• Focus on explaining the different types of arguments in Python (positional, keyword, default, and variable-length) and how they can be used in FastAPI routes.
• Give examples to illustrate the usage of different types of arguments in function definitions and calls within the context of FastAPI.

Sample Answer: In my experience, Python’s flexibility with function arguments significantly enhances FastAPI’s capability to handle various types of requests. Python supports positional arguments, which are mandatory and must be passed in the correct order. For example, in a FastAPI route, I can define a function to take specific query parameters as positional arguments to process a request.

Python also allows for keyword arguments, where the order of the arguments does not matter, and default arguments, which have a default value if not provided by the caller. This is extremely useful in FastAPI when you want to make some query parameters optional without having to handle missing arguments manually.

Finally, Python supports variable-length arguments (*args for a list of arguments and **kwargs for a dictionary of keyword arguments) which are perfect for scenarios where the number of inputs may vary. In FastAPI, I leverage this feature to accept a dynamic set of query parameters in a single endpoint, making my API more flexible and adaptable to different client needs.

Conclusion

In wrapping up our exploration of the top 33 FastAPI interview questions and answers, it’s clear that a deep understanding of FastAPI, its principles, and practical application is crucial for developers looking to excel in modern web development. These questions not only prepare you for job interviews but also deepen your comprehension of FastAPI, encouraging a more efficient and effective use of this powerful framework. Whether you’re a beginner eager to dive into the world of asynchronous programming or an experienced developer aiming to refine your skills, mastering these questions will undoubtedly set you apart in the competitive tech landscape. Remember, continuous learning and hands-on practice are key to staying ahead in the ever-evolving field of software development.