Table of Contents
FizzBuzz catches candidates who can't code at all. It doesn't separate junior from senior. Here are interview questions that actually test real Python expertise - the kind needed for production systems.
Core Python Questions
List comprehension creates entire list in memory. Generator yields one item at a time (memory efficient). Use generator for large sequences, list for multiple passes.
Via __enter__/__exit__ or contextlib.contextmanager. Resource management (file handles, database connections, locks).
Async & Concurrency Questions
threading: I/O-bound, shared memory, GIL limits CPU work. multiprocessing: CPU-bound, separate memory. asyncio: high-concurrency I/O, single-threaded event loop.
time.sleep() blocks the entire event loop. All other coroutines wait. Use asyncio.sleep() for non-blocking delay.
Design & Architecture Questions
Token bucket or sliding window. Redis for distributed rate limiting. Discuss thread-safety, performance, and accuracy trade-offs.
Decorator or context manager. Track attempt count, calculate delay, handle different exception types, max retries, jitter.
Testing & Reliability Questions
Mock external dependencies, isolate network calls, test failure scenarios, and validate retry behavior.
Unit tests validate isolated logic while integration tests verify interactions between components.
Debugging & Problem-Solving
py-spy or cProfile for profiling. Check for infinite loops, busy-waiting, or inefficient data structures. Use logging to narrow down.
Check logs for error patterns. Add timing instrumentation. Check for external dependency latency. Look for connection pool exhaustion.
How To Evaluate Answers
- ✦ Look for trade-off discussions instead of memorized definitions
- ✦ Strong candidates mention production failures and lessons learned
- ✦ Senior engineers discuss monitoring and observability
- ✦ Architect-level candidates connect technical choices to business outcomes
Test Real Python Expertise
Good Python engineers write code that works. Great Python engineers write code that's maintainable, performant, and debuggable. Offline Pixel pre-vets Python engineers on these exact skills. Raise a request, talk to candidates, fund the project, and approve payment when the work is done.
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