150+ Python Interview Questions I Wish I Had Before My FAANG Interview (Free Preview Inside)

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I spent 3 months preparing for Python developer interviews. The biggest problem? Most resources give you toy problems that never appear in real interviews.

So I compiled 150+ questions from actual interviews at FAANG, startups, and mid-level companies. Here's a free preview of the hardest categories — and what most candidates get wrong.

❌ What Most Python Interview Prep Gets Wrong

LeetCode-only prep — you can solve two-pointer problems but can't explain Python's GIL
Missing async/await — every modern Python role asks about it, most candidates freeze
No system design for Python — interviewers expect Python-specific patterns, not generic answers

🐍 The 4 Categories That Actually Matter

1. Python Internals (40+ questions)

The questions interviewers love to ask because they reveal deep understanding:

# Q: What happens when multiple threads try to modify the same list?
# Hint: Think about the GIL

import threading
data = []

def append_value(val):
    for _ in range(100):
        data.append(val)

threads = [threading.Thread(target=append_value, args=(i,)) for i in range(5)]
for t in threads:
    t.start()
for t in threads:
    t.join()

print(len(data))  # What do you expect? Is it always 500?

What interviewers want to hear: You understand the GIL prevents true parallelism for CPU-bound tasks, that list.append() is thread-safe for the CPython implementation (due to the GIL), but that this is an implementation detail, not a language guarantee.

2. Concurrency & Async (30+ questions)

Modern Python is async. Can you explain the difference?

# Q: When would you use threading vs multiprocessing vs asyncio?

# Threading — I/O-bound tasks (network requests, file reads)
import threading

def fetch_url(url):
    # Network I/O — threading works well here
    response = requests.get(url)  # Releases GIL during I/O
    return response.json()

# Multiprocessing — CPU-bound tasks (data processing, computation)
from multiprocessing import Pool

def process_data(chunk):
    # CPU-intensive — needs true parallelism
    return [x * 2 for x in chunk]

# Asyncio — High-concurrency I/O (100s of concurrent requests)
import asyncio

async def fetch_many(urls):
    tasks = [asyncio.create_task(fetch_one(url)) for url in urls]
    return await asyncio.gather(*tasks)

3. FastAPI & Modern Python (20+ questions)

If you're interviewing for a backend Python role, FastAPI questions are guaranteed:

# Q: How would you implement rate limiting in FastAPI?

from fastapi import FastAPI, Request, HTTPException
from time import time

app = FastAPI()
rate_limit_store = {}

@app.middleware("http")
async def rate_limit_middleware(request: Request, call_next):
    client_ip = request.client.host
    now = time()
    window = 60  # 1 minute
    max_requests = 100

    if client_ip in rate_limit_store:
        requests_in_window = [t for t in rate_limit_store[client_ip] if now - t < window]
        if len(requests_in_window) >= max_requests:
            raise HTTPException(status_code=429, detail="Rate limit exceeded")
        requests_in_window.append(now)
        rate_limit_store[client_ip] = requests_in_window
    else:
        rate_limit_store[client_ip] = [now]

    return await call_next(request)