How would you define a function in Python that takes no parameters and has no return statement?
- def my_function():
- def my_function(None):
- def my_function(param1, param2):
- def my_function(void):
In Python, you define a function using the def keyword, followed by the function name and parentheses, even if it takes no parameters. For a function with no return statement, it implicitly returns None.
How would you define a class variable that is shared among all instances of a class in Python?
- As a global variable outside the class
- As a local variable inside a method
- Inside the constructor method using self
- Outside of any method at the class level
In Python, you define a class variable outside of any method, directly within the class, and it is shared among all instances of the class. It is accessible as ClassName.variable_name.
How would you create an instance of a metaclass in Python?
- Metaclasses are instantiated automatically when you define a class.
- Use the create_metaclass_instance() function.
- Use the metainstance() method.
- You cannot create an instance of a metaclass.
In Python, you typically do not create instances of metaclasses directly. Metaclasses are instantiated automatically when you create a new class by inheriting from them. Attempting to create an instance of a metaclass directly is not a common practice.
How would you create a decorator to measure the execution time of a function?
- By adding timestamps manually at the beginning and end of the function.
- By using the @timer decorator.
- By wrapping the function with timeit module functions.
- Python does not support measuring execution time with decorators.
You can create a decorator to measure execution time by adding timestamps manually at the start and end of the function, then calculating the time elapsed. This allows you to track how long a function takes to execute.
How would you chain multiple decorators on a single function?
- By defining an array of decorators and applying them using a loop.
- By wrapping the function in multiple decorator functions within the function definition, e.g., function myFunction() { return decorator1(decorator2(innerFunction)); }
- JavaScript doesn't support chaining multiple decorators.
- Using the @ symbol and listing decorators one after another before a function definition, e.g., @decorator1 @decorator2 function myFunction() {...}
In JavaScript, you can chain multiple decorators by using the @ symbol and listing them one after another before a function definition. This is a common technique in modern JavaScript to apply multiple decorators to a single function.
How would you apply a decorator to a class method that needs to access the class itself?
- Use a class method decorator with @classmethod
- Use a function decorator with @func
- Use a property decorator with @property
- Use a static method decorator with @staticmethod
To apply a decorator to a class method that needs to access the class itself, you should use a class method decorator with @classmethod. Class methods have access to the class itself as their first argument, conventionally named cls. Static methods do not have access to the class, and property decorators are used to define getter, setter, and deleter methods for class attributes. Function decorators without specifying @func are not standard in Python.
How would you analyze the reference count of an object in Python to debug memory issues?
- Reference count analysis is not relevant for debugging memory issues in Python.
- Use the gc module to manually increment and decrement the reference count.
- Utilize the sys.getrefcount() function to inspect the reference count.
- Write custom code to track object references in your application.
You can use the sys.getrefcount() function to inspect the reference count of an object in Python. It's a built-in way to gather information about an object's reference count. Options 1 and 4 are not recommended practices, and Option 3 is incorrect since reference count analysis is indeed relevant for debugging memory issues.
How is a generator function different from a normal function in Python?
- A generator function is a built-in Python function
- A generator function is defined using the generator keyword
- A generator function returns multiple values simultaneously
- A generator function yields values lazily one at a time
A generator function differs from a normal function in that it uses the yield keyword to yield values lazily one at a time, allowing it to generate values on-the-fly without consuming excessive memory.
How does a metaclass differ from a class in Python?
- A class can be instantiated multiple times.
- A metaclass can be instantiated multiple times.
- A metaclass defines the structure of a class, while a class defines the structure of an instance.
- A metaclass is an instance of a class.
In Python, a metaclass is a class for classes. It defines the structure and behavior of classes, while a regular class defines the structure of instances created from it. A metaclass is used to customize class creation and behavior.
How can you invoke the method of a superclass from a subclass?
- By calling the superclass method directly
- By importing the superclass module
- By using the extends keyword
- Using the super() function
In Python, you invoke the method of a superclass from a subclass using the super() function. This allows you to access and call methods from the superclass within the subclass.
How can you integrate a Python back-end with a Single Page Application (SPA) framework like Angular or React?
- Create RESTful APIs
- Embed Python code in SPA components
- Use SOAP protocols
- Utilize Django templates
To integrate a Python back-end with an SPA framework like Angular or React, you should create RESTful APIs. This allows the front-end to communicate with the back-end through standardized HTTP requests, enabling data retrieval and manipulation.
How can you implement a stack such that you can retrieve the minimum element in constant time?
- It's not possible
- Using a linked list
- Using a priority queue
- Using an additional stack
You can implement a stack that allows retrieving the minimum element in constant time by using an additional stack to keep track of the minimum values. Whenever you push an element onto the main stack, you compare it with the top element of the auxiliary stack and push the smaller of the two. This ensures constant-time retrieval of the minimum element.