5 technical interview questions with model answers — practise the English vocabulary and structure of strong FastAPI Developer responses.
0 / 5 completed
1 / 5
The interviewer asks: "Why does async/await matter in FastAPI, and when should you use it?" Which answer best demonstrates FastAPI Developer expertise?
Option B is strongest because it covers concurrency without threads, the ASGI/Starlette foundation, how FastAPI handles sync routes via thread pool executor, the CPU-bound limitation with ProcessPoolExecutor, and the critical rule about not blocking inside coroutines. Option A is incorrect — claiming async is always faster and recommending universal async def usage would cause issues when calling synchronous blocking code inside coroutines. Option C correctly identifies use cases but doesn't explain the mechanism or the when-NOT-to-use-async caveat, which is what distinguishes intermediate from advanced knowledge. Option D discusses the GIL at length, which is technically adjacent but not relevant to the async/await question in FastAPI context — it reads like a Python internals tangent. FastAPI Developer interview best practice: always mention that regular def routes run in a thread pool executor, not inline — this surprises many candidates and shows depth.
2 / 5
The interviewer asks: "How does Pydantic improve data handling in FastAPI applications?" Which answer best demonstrates FastAPI Developer expertise?
Option B is strongest because it covers type-annotation-driven validation with concrete examples (EmailStr, Field constraints), the 422 response, automatic JSON serialisation, OpenAPI doc generation, and the Pydantic v2 Rust performance improvement — showing awareness of the current ecosystem. Option A makes a comparison to Marshmallow that is loosely true but signals the candidate knows Marshmallow better than Pydantic; FastAPI interviews expect Pydantic-native knowledge. Option C explains the basic validation flow correctly but misses serialisation, OpenAPI integration, and Field() constraints — a partial answer. Option D focuses entirely on v1-to-v2 migration syntax without explaining why Pydantic matters or how it improves data handling, which is what was actually asked. FastAPI Developer interview best practice: mention that FastAPI uses Pydantic models to auto-generate the OpenAPI schema — this demonstrates you understand the full developer experience benefit.
3 / 5
The interviewer asks: "Can you describe FastAPI's dependency injection system and its advantages?" Which answer best demonstrates FastAPI Developer expertise?
Option B is strongest because it explains Depends(), automatic sub-dependency graph resolution, the generator/yield pattern for session management, dependency_overrides for testing, async dependency support, and lifespan management — a complete and practical answer. Option A conflates FastAPI's parameter parsing (path, query, body) with the dependency injection system; these are different mechanisms and mixing them up signals a surface-level understanding. Option C correctly identifies Depends(), request-scoped caching, and two use cases, but misses the testing override capability which is a major advantage and what separates FastAPI DI from simple function parameters. Option D draws a Spring comparison that may confuse the interviewer and doesn't add information about FastAPI's actual capabilities — cross-framework comparisons belong only at the end of an answer, not as the main content. FastAPI Developer interview best practice: always mention app.dependency_overrides — it's the feature that makes FastAPI DI genuinely testable.
4 / 5
The interviewer asks: "How would you implement OAuth2 authentication with JWT tokens in FastAPI?" Which answer best demonstrates FastAPI Developer expertise?
Option B is strongest because it covers the full FastAPI implementation: OAuth2PasswordBearer, python-jose for JWT encoding/decoding, exp and sub claims, passlib/bcrypt for password hashing, the get_current_user dependency pattern, and OAuth2PasswordRequestForm — a complete and deployable picture. Option A recommends third-party libraries, which may be valid in production but in an interview context signals an unwillingness to explain the underlying mechanism; interviewers want to know you understand OAuth2+JWT, not that you can install a package. Option C explains JWT structure accurately but is completely framework-agnostic — the interviewer asked about FastAPI specifically and this answer would fit any language or framework. Option D focuses exclusively on refresh token implementation, which is a valid advanced topic but skips the fundamental access token flow that was actually asked about. FastAPI Developer interview best practice: lead with OAuth2PasswordBearer and the Depends pattern — it shows you know the FastAPI-idiomatic approach, not just generic JWT knowledge.
5 / 5
The interviewer asks: "How would you compare FastAPI to Flask and Django in a technical interview?" Which answer best demonstrates FastAPI Developer expertise?
Option B is strongest because it frames each framework's strengths clearly (async-first + OpenAPI for FastAPI, batteries-included for Django, minimal flexibility for Flask), ties the comparison to use case, mentions performance benchmarks in context, and ends with a sound principle — choose based on needs not novelty. Option A is a common candidate mistake: claiming FastAPI is always the best choice shows bias and lack of nuance, which an experienced interviewer will push back on immediately. Option C correctly identifies Django's ORM and admin advantage but gives an incomplete comparison — Flask isn't mentioned and there's no performance context or decision framework. Option D is factually wrong claiming all three use WSGI (Flask is WSGI, FastAPI is ASGI, Django supports both) and drifts into async history without providing a clear recommendation structure. FastAPI Developer interview best practice: structure the comparison as a decision matrix, not a ranking — this demonstrates you can advise a team rather than just advocate for your favourite tool.