Python Interview Questions.
70+ expert-level questions covering Core Python internals, AsyncIO, and Django 5.x architecture.
Course Overview: Backend Engineering Mastery
Module 01 // Core Python Internals
The GIL prevents multiple native threads from executing Python bytecodes at once. PEP 703 (Java-style 'No-GIL') allows for true parallel execution in multi-core systems, making Python a much stronger contender for high-performance computing without relying on multiprocessing.
Decorators are higher-order functions that take a function as an argument and return a modified version of it. Under the hood, they leverage Python's first-class function objects and closures to wrap functionality without changing the original source code.
All generators are iterators, but not all iterators are generators. Generators use the 'yield' keyword to produce values lazily, which is significantly more memory-efficient than creating a full list in memory for large datasets.
Module 02 // Asynchronous Programming (AsyncIO)
Unlike threading, AsyncIO uses 'Cooperative Multitasking'. A single thread switches between tasks when they hit an 'await' point (like I/O wait). This avoids the overhead of context switching and locking required in multi-threaded environments.
Use AsyncIO for I/O-bound tasks (web scraping, database queries) where you are waiting on external sources. Use Multiprocessing for CPU-bound tasks (data processing, encryption) to leverage multiple CPU cores simultaneously.
Module 03 // Django Architectural Mastery
Middleware follows a 'Onion' structure. On the request phase, it executes from top to bottom. On the response phase (after the view), it executes in reverse order from bottom to top.
Use 'select_related' for foreign keys (one-to-one/many-to-one) to do a SQL JOIN in a single query. Use 'prefetch_related' for many-to-many or many-to-one reverse lookups, which performs separate queries and joins them in Python.
The N+1 problem occurs when you fetch a list of objects and then perform a separate query for each object's related data. It is solved by using 'select_related' or 'prefetch_related' to fetch all required data in as few queries as possible.
Module 04 // Modern Features & POO (OOP)
MRO is the order in which Python looks for a method in a class hierarchy. Python uses the C3 Linearization algorithm to ensure that 'super()' calls are predictable and avoid the 'Diamond Problem' in multiple inheritance.
Dunder methods (e.g., __init__, __str__, __call__) allow you to overload operators and define built-in behaviors for your objects. For example, __call__ makes an instance of a class callable like a function.
Module 05 // Recruiter's Screening Room (Junior & Confirmed)
Lists are mutable (you can change, add, or remove elements). Tuples are immutable (fixed size and content after creation). Tuples are generally faster and safer for data that shouldn't change.
The '==' operator checks for value equality (if the contents are the same). The 'is' operator checks for identity (if both variables point to the same object in memory).
Python dictionaries use a Hash Table. Keys are hashed into integers to provide near-instant (O(1)) lookup time, making them extremely efficient for large data sets.
Decorators are functions that wrap another function to extend its behavior without permanently modifying it. They are commonly used for logging, access control, and timing.
It creates an isolated space for a project's dependencies, preventing version conflicts between different Python projects on the same machine.
Module 06 // Enterprise Security & Scaling
For CSRF, use 'Double Submit Cookie' via Django's CSRF_COOKIE_HTTPONLY=False and a custom header in axios. For XSS, rely on React's automatic escaping, but use strict 'Content-Security-Policy' (CSP) headers to prevent unauthorized script execution and data exfiltration.
Django handles the stateless web layer. Celery manages long-running background tasks (like PDF generation). Redis acts as the message broker. By adding more Celery workers and scaling the Django web containers independently, you can handle millions of concurrent requests.
Partitioning divides a large table into smaller pieces within a single DB (e.g., by date). Sharding distributes data across completely different database servers. Use partitioning for manageability and indexing speed; use sharding when a single DB server hits its I/O or storage limit.
Advanced Insight: The AsyncIO Loop
Modern Python performance is driven by Cooperative Multitasking. Unlike traditional threading where the OS forcibly switches between threads, asyncio allows tasks to voluntarily yield control back to the event loop when they are waiting for I/O.
# Efficient I/O with AsyncIO
import asyncio
async def fetch_data(id):
print(f"Start {id}")
await asyncio.sleep(1) # Simulated I/O
print(f"End {id}")
return {"id": id, "data": "success"}
async def main():
# Run 5 tasks concurrently
results = await asyncio.gather(*(fetch_data(i) for i in range(5)))
print(f"Fetched {len(results)} items")
asyncio.run(main())By using asyncio.gather, we initiate all five network requests simultaneously. The event loop monitors these requests and resumes each fetch_data task as its I/O completes. This single-threaded approach is highly scalable and avoids the memory overhead of spawning multiple OS threads.
Python Engineering FAQ
PEP 703 introduces a build-time flag to disable the GIL entirely. Existing single-threaded code runs unchanged with ~5% overhead. Multi-threaded code gains true parallelism but requires thread-safety discipline — shared mutable state without locks will cause race conditions. The practical recommendation: start auditing your thread-safety patterns now, before No-GIL becomes the default in Python 3.15+.
Python uses a dual strategy: Reference Counting (immediate cleanup when refcount hits 0) plus a Generational Garbage Collector for breaking circular references. Objects are allocated from a private heap managed by CPython's memory allocator. Understanding this is critical for debugging memory leaks — use tracemalloc and objgraph to identify retention chains.
Scaling a Python application in 2026 requires more than just clean code; it requires Deterministic Profiling. Use the cProfile module to identify CPU bottlenecks and scalene for a combined CPU, GPU, and memory profile. In high-throughput Django environments, pay close attention to Context Switching overhead in AsyncIO tasks — too many concurrent tasks can lead to 'Event Loop Starvation' where no single task makes progress.
For performance-critical hot paths, the modern standard is to write a Rust extension using PyO3. This allows you to maintain Python's developer velocity for the application layer while achieving C-level performance for heavy computation. Libraries like Pydantic and Polars have already proven this model's superiority, achieving 10-100x speedups over pure Python implementations.
Use @dataclass for internal data containers where type hints are documentation-only. Use Pydantic for external-facing data (API payloads, config files) where runtime validation is essential. Pydantic v2 runs on Rust (via pydantic-core), making it 5-50x faster than v1 while providing automatic serialization, deserialization, and comprehensive error messages.
Start with django-debug-toolbar to identify N+1 queries. Use select_related() for ForeignKey joins and prefetch_related() for ManyToMany. For complex analytics, drop to raw SQL with .raw() or use Django's Window functions. Always add db_index=True on fields you filter/order by, and use .only() to limit SELECT columns.
Follow the "src layout" pattern: place all source code under src/your_package/ with separate directories for domain logic, infrastructure, and API layers. Use dependency injection (via dependency-injector or manual constructor injection) to decouple layers. Keep your pyproject.toml as the single source of truth for dependencies, scripts, and tool configuration.
Static type checkers (mypy, Pyright) catch entire categories of bugs before runtime — incorrect argument types, missing return values, and attribute access on None. In large codebases, strict type checking reduces production bugs by 15-30%. Combine with typing.Protocol for structural subtyping and TypeGuard for runtime narrowing to get the best of both static and dynamic Python worlds.
Master the Ecosystem.
True Python mastery is about more than syntax—it's about understanding how the memory model and event loop interact with the OS. Format your backend API payloads seamlessly with our JSON Formatter.
Feedback
M. Leachouri
Founder & Chief Architect"I built Kodivio because professional tools shouldn't come at the cost of your privacy. Our mission is to provide enterprise-grade utilities that process data exclusively in your browser."
M. Leachouri is an Expert Web Developer, Data Scientist Engineer, and Systems Architect with a deep specialization in DevOps and Cybersecurity. With over a decade of experience building scalable distributed systems and Zero-Trust architectures, he engineered Kodivio to bridge the gap between high-performance computing and absolute user sovereignty.