Python Expertise: Senior Developer Questions and Answers

Core Python Concepts

1. What are the key differences between deep copy and shallow copy?
2. Explain Python's memory management model.
3. How do Python's data structures (list, tuple, set, dict) differ in terms of performance and usage?
4. What is the difference between is and == in Python?
5. Explain the Global Interpreter Lock (GIL) and its implications.
6. How does Python implement multiple inheritance?
7. What are metaclasses, and when would you use them?
8. Explain decorators and provide examples of their usage.
9. What is the difference between iterables and iterators?
10. How does Python's garbage collector work?

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Advanced Programming

1. Explain context managers and how to create custom ones.
2. How do you implement a singleton pattern in Python?
3. What are coroutines, and how are they different from generators?
4. Explain the concept of monkey patching in Python.
5. How do you optimize the performance of Python code?
6. What is duck typing, and how is it used in Python?
7. Explain abstract base classes (ABCs) and their purpose.
8. What is the difference between @staticmethod, @classmethod, and instance methods?
9. How do you create thread-safe code in Python?
10. What are slots, and how do they improve memory usage?

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Performance Optimization

1. How do you identify and fix bottlenecks in Python code?
2. What tools do you use for profiling Python code?
3. Explain the trade-offs between NumPy and pure Python.
4. How do you optimize code using list comprehensions?
5. What is cython, and how does it improve performance?
6. How do you handle large data processing in Python?
7. What is lazy evaluation, and how can it improve performance?
8. Explain the impact of mutable vs. immutable objects on performance.
9. How do you optimize I/O-bound tasks in Python?
10. What is vectorization, and how does it improve computational efficiency?

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Concurrency and Parallelism

1. What are the differences between threading, multiprocessing, and asyncio?
2. How do you avoid race conditions in multi-threaded Python programs?
3. Explain async/await and its use cases.
4. What is the role of the queue module in concurrency?
5. How do you implement producer-consumer patterns in Python?
6. How does the concurrent.futures module simplify concurrent programming?
7. Explain the concept of event loops in Python.
8. What are the limitations of the GIL, and how can you overcome them?
9. How do you use semaphores to manage resources?
10. Explain the concept of task scheduling in asyncio.

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Data Science and Libraries

1. What is the difference between Pandas Series and DataFrame?
2. How do you handle missing data in Pandas?
3. What are the core differences between NumPy arrays and Python lists?
4. How does matplotlib differ from seaborn?
5. What are the key benefits of using SciPy over NumPy?
6. Explain how Scikit-learn handles feature scaling.
7. How does Python handle large-scale machine learning tasks?
8. What are TensorFlow and PyTorch, and when would you use them?
9. Explain Dask and its role in parallel computing.
10. How do you implement data pipelines in Python?

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Security and Best Practices

1. How do you prevent SQL injection in Python?
2. What is the role of hashlib in securing data?
3. How do you securely store API keys in Python applications?
4. What is the purpose of the secrets module?
5. How do you mitigate buffer overflows in Python?
6. What is input validation, and how do you implement it?
7. Explain the role of SSL/TLS in securing Python applications.
8. How do you prevent injection attacks in Python web applications?
9. What is CSRF, and how can it be prevented in Python web frameworks?
10. How do you handle data encryption in Python?

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Testing and Debugging

1. What are the key differences between unittest and pytest?
2. How do you write parameterized tests in Python?
3. Explain the purpose of mocking in unit tests.
4. How does pdb simplify debugging in Python?
5. What is the role of doctests in Python testing?
6. How do you measure code coverage in Python?
7. What is the role of the assert keyword in debugging?
8. How do you use profiling tools to debug performance issues?
9. What is flaky testing, and how do you mitigate it?
10. How do you debug memory leaks in Python applications?

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Real-World Challenges and Scenarios

1. How do you design a Python microservices architecture?
2. What are the challenges of handling real-time data in Python?
3. How do you deploy Python applications in a serverless environment?
4. What are the best practices for handling large-scale logging in Python?
5. How do you manage dependency conflicts in Python projects?
6. How do you scale Python applications in containerized environments?
7. How do you handle dynamic configurations in Python?
8. What are best practices for CI/CD pipelines in Python projects?
9. How do you manage data consistency across distributed systems in Python?
10. How do you implement fault-tolerant applications using Python?

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