Python Interview Questions – Industry Expert Level (25 Q&A)
Architecture, Performance, Debugging, CI/CD, Automation & Production-grade Python.
1️⃣ How do you design a scalable Python application architecture?
- Layered architecture (API → Service → Domain → Infra)
- SOLID principles
- Dependency Injection
- Config, logging & business logic separation
class PaymentService:
def __init__(self, gateway):
self.gateway = gateway
def pay(self, amount):
return self.gateway.process(amount)
pip install fastapi uvicorn
2️⃣ How do you handle memory leaks in long-running Python services?
- Use tracemalloc
- Analyze object growth
- Fix circular references
- Prefer generators
import tracemalloc
tracemalloc.start()
python memory_test.py
3️⃣ Multithreading vs Multiprocessing – how do you design?
- Threading → I/O-bound
- Multiprocessing → CPU-bound (bypass GIL)
from multiprocessing import Pool
Pool(4).map(pow, [1,2,3,4], [2,2,2,2])
4️⃣ How do you debug Python slowdown in production?
- Enable profiling
- Detect blocking calls
- Optimize hot paths
python -m cProfile app.py
5️⃣ How do you design Python REST APIs for enterprise scale?
- FastAPI
- Async I/O
- Pydantic validation
- Rate limiting
@app.get("/health")
async def health():
return {"status": "UP"}
uvicorn main:app --reload
6️⃣ How do you automate Python tests in CI/CD?
- pytest
- CI integration
- Fail pipeline on test failure
pytest --maxfail=1 --disable-warnings
7️⃣ How do you troubleshoot Python dependency conflicts?
- Virtualenv
- Freeze versions
- pip-tools
pip freeze > requirements.txt
8️⃣ Exception chaining for root cause analysis?
try:
int("abc")
except ValueError as e:
raise RuntimeError("Parsing failed") from e
9️⃣ Retry & exponential backoff design?
import time
for i in range(3):
try:
call_api()
break
except:
time.sleep(2**i)
🔟 How do you optimize Python DB performance?
- Connection pooling
- Batch inserts
- Index tuning
pip install sqlalchemy
11️⃣ Debug Python deadlocks?
Analyze thread dumps, avoid nested locks, use timeouts.
12️⃣ Python automation framework design?
POM, reusable utilities, data-driven tests.
13️⃣ Large file processing?
for line in open("big.log"):
process(line)
14️⃣ Python app security?
export API_KEY=xyz
15️⃣ Profile memory vs CPU?
python -m memory_profiler app.py
16️⃣ Package import errors?
which python
17️⃣ Event-driven Python systems?
pip install kafka-python
18️⃣ Log correlation?
logging.info("req_id=%s", rid)
19️⃣ Unicode issues?
open("file.txt", encoding="utf-8")
20️⃣ Optimize startup time?
python -X importtime app.py
21️⃣ API testing without UI?
curl http://localhost:8000/health
22️⃣ Plugin architecture?
import importlib
23️⃣ Graceful shutdown?
signal.signal(signal.SIGTERM, handler)
24️⃣ Debug hanging process?
kill -SIGUSR1 <pid>
25️⃣ Production-ready Python checklist?
✔ Logging ✔ Monitoring ✔ Testing ✔ CI/CD ✔ Containers
docker build -t python-app .