included. Some of the questions might even feel a little odd or out there. But in my experience, they're great at helping you figure out who's really ready for the job.
The series is split into three parts based on experience level:
- Python Interview Questions: Part I (Junior)
- Python Interview Questions: Part II (Middle)
- Python Interview Questions: Part III (Senior)
Got a question you think should be here? Or something you always ask in interviews? Let me know — I'd love to hear your thoughts!
We all know that a Senior developer is not only a technical role with years of experience and knowledge about their tools, they also have mentoring skills, some management skills, and they tend to have some architectural skills, etc. My questions here are only about the technical side of Senior Python developers.
Questions
1. Discussion Questions
Q: Python is often called an easy language. Do you agree with this statement? Why or why not?
Q: What are some pitfalls and limitations of Python as a language?
Q: How does a framework differ from a library? Can you provide examples?
Q: How do you ensure the maintainability of a large codebase? Discuss tools, static analysis, and coding practices you rely on.
Q: What is monkey patching? How to use it in Python? Example? Is it ever a good idea?
2. Python Internals
Q: How are dict and set implemented internally? What is the complexity of retrieving an item? How much memory do these structures consume?
Q: Does Python support multiple inheritance? How does it solve the diamond problem?
Q: What is MRO (Method Resolution Order) in Python? How does it work?
Q: Does Python have an assignment operator? How is assignment in Python different from C or C++?
Q: What are descriptors? How do they differ from decorators?
Q: How are function arguments passed in Python — by value or by reference? Explain with examples.
Q: What are .pth files in Python?
3. Memory Management
Q: How would you identify and resolve memory leaks in a production application?
Q: You have a memory leak in the working production application on one of your company servers. How would you start debugging it?
Q: You need to process a file that is too large to fit into memory. How would you handle this in Python?
- Follow-up 1: How can you ensure the solution is memory-efficient and scalable?
- Follow-up 2: If the file is compressed (e.g., a .gz or .zip file), how would you process it without fully decompressing it in memory?
- Follow-up 3: How would you adapt your solution if the file is being streamed over a network instead of being locally stored?
4. Performance and Optimization
Q: How would you profile a Python application to identify performance bottlenecks? What tools or techniques would you use?
Q: What is __pycache__ in Python, and why does Python generate .pyc files? How do they improve performance?
Q: What is string interning? Why does Python use it?
Q: Why doesn't Python support tail recursion optimization? How would you implement a similar effect manually?
Q: You're asked to optimize a Python web application under high load. How would you approach this, and what tools might you use?
Q: What does the PYTHONOPTIMIZE flag do?
Q: What advantages do NumPy arrays offer over (nested) Python lists?
5. Packaging and Distribution
Q: How do you distribute Python code effectively?
Q: How does Python manage binary dependencies? Discuss wheels and eggs.
Q: What are Python's package managers, and which one would you recommend?
Q: How do you handle transitive dependencies in Python projects?
Q: What is Cython, IronPython, and PyPy? Why do these alternate implementations exist?
Q: Explain how to access a Python module from C and vice versa.
6. Functional Programming
Q: Is Python a functional programming language? Explain with examples.
Q: What are the key pitfalls or limitations of writing functional code in Python?
Q: How can you implement functional concepts like immutability or higher-order functions in Python?
7. Concurrency
Q: What is the difference between concurrency and parallelism?
- Follow-up: Can Python achieve true parallelism? Why or why not?
Q: What is the GIL, and how does it affect concurrency and async programming in Python? How does Python's asyncio handle concurrency despite the GIL?
- Follow-up: Why does GIL still exist?
Q: Compare and contrast async programming, multi-threading, and multi-processing in Python.
Q: Imagine a producer thread is generating data faster than a consumer can process it. How would you handle this scenario in Python?
- Follow-up: How would you handle it in an async setup?
Q: How do you debug async code effectively? Mention tools or techniques you've used.
Q: What is backpressure in async systems, and how can you handle it?
Q: Discuss how async programming can improve the scalability of a web application.
Q: Write a function using asyncio.Queue to implement a producer-consumer pattern with multiple producers and consumers.
Q: How would you design a high-performance async API server? What frameworks would you use, and why?
Q: How can you integrate async programming with synchronous libraries or codebases?
Q: What are the limitations of async programming in Python, and how would you work around them?
8. Coding Challenges
Q: Give an example of a filter and reduce over an iterable object.
Q: Write a function that reverses the generator?
Q: You need to implement a function that should use a static variable (for example, a call counter). You cannot write any code outside the function and you do not have information about external variables (outside your function). How to do it?
Q: What methods and in what order are called when print (A() + B()) is executed?
Q: How to implement a dictionary from scratch using core Python?
Q: What will be the output of the following code?
>>> a = [[]] * 3
>>> a[1].append(1)
>>> print(a) # [[1], [1], [1]
Q: Place the following functions below in order of their efficiency. How would you test your answer?
def f1(arr):
l1 = sorted(arr)
l2 = [i for i in l1 if i < 0.5]
return [i * i for i in l2]
def f2(arr):
l1 = [i for i in arr if i<0.5]
l2 = sorted(l1)
return [i * i for i in l2]
def f3(arr):
l1 = [i * i for i in arr]
l2 = sorted(l1)
return [i for i in l1 if i < (0.5*0.5)]
Q: Write a one-liner that will count the number of capital letters in a file. Your code should work even if the file is too big to fit in memory.
Q: What will be the output of the following code? Why? Is this inheritance?
class C:
pass
type (C())
type (C)
Q: What will be the output of the following code?
big_num_1 = 1000
big_num_2 = 1000
small_num_1 = 1
small_num_2 = 1
big_num_1 is big_num_2
small_num_1 is small_num_2
Q: How is this possible?
_MangledGlobal__mangled = 23
class MangledGlobal:
def test(self):
return __mangled
>>> MangledGlobal().test()
23
Q: You saw the following piece of code. What is wrong with this code? Why is it needed?
if __debug__:
assert False, ("error")
You may also find python programmer jobs with Jooble
Additional materials
- Fluent Python by Luciano Ramalho
- Python Distilled by David Beazley
- Grokking Concurrency by Kirill Bobrov
- Effective Python by Brett Slatkin
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