How would you handle missing data for a numerical feature in a dataset before training a machine learning model?
- Ignore missing data, it won't affect the model
- Remove the rows with missing data
- Replace missing values with a random value
- Replace missing values with the mean of the feature
Handling missing data is crucial. Replacing missing values with the mean of the feature is a common practice as it retains data and doesn't introduce bias, especially in numerical features. Removing rows or using random values can lead to loss of information or noise.
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 implement a custom loss function in a TensorFlow or PyTorch model?
- Call the loss function during evaluation
- Define a function that calculates the loss
- Use the built-in loss functions
- Use the optimizer to define a loss function
To implement a custom loss function, you need to define a function that calculates the loss based on your specific requirements. This function is used in the training loop to compute the loss during training.
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 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 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 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 can you dynamically create a new type (class) at runtime in Python?
- Using closures
- Using decorators
- Using list comprehensions
- Using metaclasses
You can dynamically create a new type (class) at runtime in Python by using metaclasses. Metaclasses allow you to define the behavior of classes themselves. Decorators are used to modify the behavior of functions or methods, not to create classes. Closures and list comprehensions are not directly related to class creation.
How can you ensure that user-uploaded files in a web application are securely handled when integrating Python back-end with front-end technologies?
- Allow direct access to uploaded files to improve performance.
- Implement proper file validation, authentication, and authorization.
- Store user-uploaded files in a publicly accessible directory.
- Use third-party services to handle file uploads.
To ensure the secure handling of user-uploaded files, you should implement proper file validation to check file types and content, authentication to ensure that only authorized users can upload files, and authorization to control who can access the files. Storing files in a publicly accessible directory can be a security risk.
In Python, which operator has the highest precedence?
- * (Multiplication)
- ** (Exponentiation)
- + (Addition)
- / (Division)
In Python, the double asterisk '**' operator, used for exponentiation, has the highest precedence among all operators. It is evaluated before other operators in an expression.