Cognizant Top 20 Python Interview Questions and Answers

Python is widely used for web development, data analysis, artificial intelligence, and more. As a Python Developer at Cognizant, you must be proficient in Python programming concepts, data structures, OOP, and advanced features like decorators and generators. Below are the top 20 Cognizant Python interview questions to help you prepare.

1. What are the key features of Python?

Python is a high-level, interpreted programming language with features like:

• Simple and easy to learn: Python's syntax is straightforward and readable.
• Dynamically typed: Variables are not required to declare types explicitly.
• Interpreted language: Python code is executed line by line.
• Extensive libraries: Python has a rich set of libraries for various domains like data science, web development, and AI.

2. How is memory managed in Python?

Python manages memory using a private heap space, and the memory allocation is handled by Python's memory manager. Python also has an automatic garbage collector that reclaims unused memory to prevent memory leaks.

3. What is PEP 8, and why is it important?

PEP 8 is Python's style guide that outlines the conventions for writing readable and consistent code. It is essential for maintaining code readability and helping developers collaborate effectively.

4. Explain the difference between a list and a tuple in Python.

• List: Mutable, can be modified after creation, supports dynamic resizing, and allows duplicate elements.
• Tuple: Immutable, cannot be modified after creation, generally faster than lists, and allows duplicate elements.

5. What are Python decorators, and how are they used?

Decorators in Python are functions that modify the behavior of another function or method. They are used to add functionality to functions or methods without modifying their structure. A decorator is applied using the @decorator_name syntax above the function definition.

Example:

def my_decorator(func):
    def wrapper():
        print("Before function execution")
        func()
        print("After function execution")
    return wrapper

@my_decorator
def say_hello():
    print("Hello, World!")

say_hello()

6. What are *args and **kwargs in Python?

• *args allows you to pass a variable number of non-keyword arguments to a function.
• **kwargs allows you to pass a variable number of keyword arguments (i.e., arguments passed as key-value pairs).

7. How does Python handle exceptions?

Python provides the try-except block to handle exceptions. Code that may raise an exception is placed in the try block, and the code to handle the exception is written in the except block.

Example:

try:
    num = 1 / 0
except ZeroDivisionError:
    print("Cannot divide by zero")

8. What is the difference between deep copy and shallow copy?

• Shallow copy: Copies the reference to the objects in the collection, so changes to the original object will reflect in the copied object.
• Deep copy: Creates a completely new copy of the objects, and changes to the original object will not affect the copied object.

9. What is the difference between is and == in Python?

• is: Checks if two variables point to the same object in memory.
• ==: Checks if the values of two variables are equal.

10. How are functions treated in Python?

In Python, functions are first-class objects, meaning they can be passed as arguments, returned from other functions, and assigned to variables. This enables features like higher-order functions and functional programming.

11. What is a lambda function in Python?

A lambda function is an anonymous function that can have any number of arguments but only one expression. It is often used for small, one-time tasks.

Example:

add = lambda x, y: x + y
print(add(5, 3))  # Output: 8

12. What are Python generators, and how do they differ from regular functions?

Generators are functions that return an iterator and allow you to iterate over data without storing it all in memory. They use the yield keyword instead of return to produce a series of results lazily.

Example:

def count_up_to(max):
    num = 1
    while num <= max:
        yield num
        num += 1

for i in count_up_to(5):
    print(i)

13. What is the Global Interpreter Lock (GIL) in Python?

The GIL is a mutex that protects access to Python objects, ensuring that only one thread can execute Python bytecode at a time. It helps with memory management but limits true parallel execution in Python's multi-threading.

14. What are the built-in data types in Python?

Python's built-in data types include:

• Numeric types: int, float, complex
• Sequence types: list, tuple, range, str
• Mapping type: dict
• Set types: set, frozenset
• Boolean type: bool
• None type: NoneType

15. Explain Python's list comprehensions.

List comprehensions provide a concise way to create lists by applying an expression to each item in an iterable, optionally filtering items based on a condition.

Example:

squares = [x**2 for x in range(10)]

16. How do you manage memory in Python?

Python has automatic memory management that includes:

• Reference counting: When an object's reference count drops to zero, it is garbage-collected.
• Garbage collector: Handles cyclic references where two objects refer to each other but are no longer reachable.

17. What are metaclasses in Python?

A metaclass is the class of a class, meaning that it defines how classes behave. Metaclasses allow you to customize class creation, enabling advanced features like class decorators or class modification at runtime.

18. How do you perform static type checking in Python?

Python 3.5 introduced type hints through the typing module, allowing developers to specify the types of variables and function return values. Tools like mypy can be used to check type consistency in the code.

Example:

def add_numbers(x: int, y: int) -> int:
    return x + y

19. What is the purpose of self in Python?

The self parameter refers to the current instance of the class. It is used to access class attributes and methods. It is automatically passed to instance methods but needs to be explicitly declared.

20. Explain method resolution order (MRO) in Python.

MRO is the order in which Python looks for a method in a class hierarchy when multiple inheritance is used. Python uses the C3 linearization algorithm to determine the order, which can be accessed using the __mro__ attribute or the mro() method.

Conclusion

These top 20 Python interview questions and answers will help you prepare for your Cognizant Python Developer interview. Focus on mastering Python's core concepts, OOP principles, and advanced features like generators and decorators to stand out in the interview process.

Good Luck!