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🐍 Python — Staff-Level Interview Questions

Deep-dive into Python's internals, concurrency models, memory management, and production patterns Designed for Staff/Principal Engineer interviews (10+ years experience)

📋 What's Inside

File Content
INTERVIEW_QUESTIONS.md 12 in-depth questions covering Python's core internals at staff level
MULTITHREADING_NOTES.md Threads, the GIL, free-threaded Python (3.13/3.14), subinterpreters and InterpreterPoolExecutor, multiprocessing start methods, synchronization patterns, and 16 interview questions
DJANGO_NOTES.md Django 5.2 LTS / 6.0: ORM, transactions, request lifecycle, DRF, migrations, caching, Celery and the 6.0 tasks framework, async Django, production patterns, and 16 interview questions
FASTAPI_NOTES.md FastAPI with Pydantic v2 and Starlette 1.x: DI and yield scopes, lifespan, async pitfalls, security, SQLAlchemy async, WebSockets, deployment, and 13 interview questions
ASYNC_BASICS.md Start here: running functions concurrently with await, gather, TaskGroup, timeouts, semaphores, to_thread, and common mistakes (all examples runnable)
ASYNCIO_NOTES.md Comprehensive guide to async/await, event loop internals, coroutines, tasks, streams, and production async patterns

Topics Covered

  • GIL & Concurrency — GIL internals, when to use threading vs asyncio vs multiprocessing, free-threaded Python, subinterpreters (see MULTITHREADING_NOTES.md)
  • Async/Await — Event loop internals, coroutine protocols, uvloop, structured concurrency (see ASYNCIO_NOTES.md for the full dedicated guide)
  • Metaclasses & Descriptors — Class creation protocols, __init_subclass__, __set_name__, descriptor protocol
  • Memory Management — CPython allocator, reference cycles, GC generations, __slots__
  • Type System — Protocol, @overload, TypeVar with constraints, variance, Self
  • C Extensions — PyObject, reference counting, ctypes vs Cython vs cffi
  • Data Model — __dunder__ protocols, context managers, async generators
  • Import System — sys.modules, finders/loaders, circular imports, namespace packages
  • Performance — Profiling strategies, JIT alternatives (PyPy, Numba), GIL-free experiments
  • Production Patterns — Dependency injection, configuration management, plugin architectures
  • Packaging — pyproject.toml, build systems, ABI compatibility, platform wheels

How to Use

  1. Read each question and try to answer before looking at the expected answer
  2. Study the code examples — they demonstrate production-quality patterns
  3. Understand the trade-offs — staff-level interviews are about why, not what
  4. Run the code snippets to internalize the concepts

Built for experienced Python engineers targeting Staff/Principal roles at top-tier companies